code stringlengths 101 5.91M |
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class EquivariantDipoleMoment(EquivariantScalar):
def __init__(self, hidden_channels, activation='silu'):
super(EquivariantDipoleMoment, self).__init__(hidden_channels, activation, allow_prior_model=False)
atomic_mass = torch.from_numpy(ase.data.atomic_masses).float()
self.register_buffer('atomic_mass', atomic_mass)
def pre_reduce(self, x, v, z, pos, batch):
for layer in self.output_network:
(x, v) = layer(x, v)
mass = self.atomic_mass[z].view((- 1), 1)
c = (scatter((mass * pos), batch, dim=0) / scatter(mass, batch, dim=0))
x = (x * (pos - c[batch]))
return (x + v.squeeze())
def post_reduce(self, x):
return torch.norm(x, dim=(- 1), keepdim=True) |
def get_runner(experiment, options=None):
runners = json.load(open('runners.json', 'r'))
return (runners[experiment][options] if (options is not None) else runners[experiment]) |
def main():
sns.set_context('paper')
sns.set_style('white')
model_versions = ['distilgpt2', 'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl']
filters = ['filtered', 'unfiltered']
for model_version in model_versions:
for filter in filters:
for split in ['dev', 'test']:
fname = f'winobias_data/attention_intervention_{model_version}_{filter}_{split}.json'
if (not os.path.exists(fname)):
print('File does not exist:', fname)
continue
with open(fname) as f:
data = json.load(f)
save_figures(data, 'winobias', model_version, filter, split)
for model_version in model_versions:
for filter in filters:
for stat in ['bergsma', 'bls']:
fname = f'winogender_data/attention_intervention_{stat}_{model_version}_{filter}.json'
if (not os.path.exists(fname)):
print('File does not exist:', fname)
continue
with open(fname) as f:
data = json.load(f)
save_figures(data, 'winogender', model_version, filter, stat) |
def test_jieba_no_ssplit():
nlp = stanza.Pipeline(lang='zh', dir=TEST_MODELS_DIR, processors={'tokenize': 'jieba'}, tokenize_no_ssplit=True, package=None)
doc = nlp(ZH_DOC)
assert ('JiebaTokenizer' == nlp.processors['tokenize']._variant.__class__.__name__)
assert (ZH_DOC_GOLD_NOSSPLIT_TOKENS == '\n\n'.join([sent.tokens_string() for sent in doc.sentences]))
assert all([(doc.text[token._start_char:token._end_char] == token.text) for sent in doc.sentences for token in sent.tokens]) |
_model
def densenet169(pretrained=False, **kwargs):
model = _densenet('densenet169', growth_rate=32, block_config=(6, 12, 32, 32), pretrained=pretrained, **kwargs)
return model |
def score_sequences(y_true: List[List[int]], y_pred: List[List[int]], metrics: Set[str]=None) -> Dict[(str, float)]:
scorers = {'accuracy': seqeval.metrics.accuracy_score, 'precision': seqeval.metrics.precision_score, 'recall': seqeval.metrics.recall_score, 'f1': seqeval.metrics.f1_score}
metrics = (metrics if (metrics is not None) else scorers)
try:
return {name: scorers[name](y_true, y_pred) for name in metrics}
except:
return {name: 0.0 for name in metrics} |
def visualize_rgb(tif_path, cut_off_value=2000, show=False, save='tmp.png', force_process_all=False):
plot = plt.figure()
src = rasterio.open(('gs://' + tif_path))
if (not force_process_all):
if ((src.width * src.height) > (3451 * 4243)):
print('skipping too large~ ', src.width, src.height, src)
return (None, None)
if ((src.width * src.height) < (500 * 500)):
print('skipping too small~ ', src.width, src.height, src)
return (None, None)
print('opening ~ ', src.width, src.height, src)
image_rgb = src.read([4, 3, 2])
(red, green, blue) = image_rgb
red[(red > cut_off_value)] = cut_off_value
blue[(blue > cut_off_value)] = cut_off_value
green[(green > cut_off_value)] = cut_off_value
red = (rasterio.plot.adjust_band(red, kind='linear') * 255).astype(np.uint8)
green = (rasterio.plot.adjust_band(green, kind='linear') * 255).astype(np.uint8)
blue = (rasterio.plot.adjust_band(blue, kind='linear') * 255).astype(np.uint8)
array = np.stack([red, green, blue], axis=0)
resolution = array.shape
rasterio.plot.show(array)
plot.tight_layout()
plt.axis('off')
if show:
plot.show()
if save:
plot.savefig(save)
return (plot, resolution) |
def extracted_glob(extracted_folder, file_patterns, src, tgt, lang):
def get_matching_pattern(file_pattern):
params = {k: v for (k, v) in [('src', src), ('tgt', tgt), ('lang', lang)] if ((('{' + k) + '}') in file_pattern)}
file_pattern = re.sub('{src:(.*?)}', ('\\1' if (lang == src) else ''), file_pattern)
file_pattern = re.sub('{tgt:(.*?)}', ('\\1' if (lang == tgt) else ''), file_pattern)
file_pattern = file_pattern.format(**params)
return file_pattern
for file_pattern in file_patterns:
if isinstance(file_pattern, tuple):
(file_pattern, lang_pairs) = file_pattern
if (f'{src}-{tgt}' not in lang_pairs):
continue
matching_pattern = get_matching_pattern(file_pattern)
if (matching_pattern is None):
continue
glob_patterns = f'{extracted_folder}/{matching_pattern}'
for f in glob.glob(glob_patterns):
(yield f) |
def autodoc_skip_member(app, what, name, obj, skip, options):
exclusions = ('yaml_constructors', 'yaml_implicit_resolvers')
exclude = (name in exclusions)
return (skip or exclude) |
class BraTSDatasetLSTM(Dataset):
__im = []
__mask = []
__im1 = []
__im3 = []
im_ht = 0
im_wd = 0
dataset_size = 0
def __init__(self, dataset_folder, train=True, keywords=['P1', '1', 'flair'], im_size=[128, 128], transform=None):
self.__file = []
self.__im = []
self.__mask = []
self.im_ht = im_size[0]
self.im_wd = im_size[1]
self.transform = transform
folder = dataset_folder
if train:
folder = (dataset_folder + 'Train/')
else:
folder = (dataset_folder + 'Test/')
max_file = 0
min_file =
for file in os.listdir(folder):
if file.endswith('.png'):
m = re.search('(P[0-9]*[_])([0-9]*)', file)
pic_num = int(m.group(2))
if (pic_num > max_file):
max_file = pic_num
if (pic_num < min_file):
min_file = pic_num
for file in os.listdir(folder):
if file.endswith('.png'):
filename = os.path.splitext(file)[0]
filename_fragments = filename.split('_')
samekeywords = list((set(filename_fragments) & set(keywords)))
if (len(samekeywords) == len(keywords)):
if ((filename_fragments[2] != str(min_file)) and (filename_fragments[2] != str(max_file))):
self.__im.append((folder + file))
file1 = (((((((filename_fragments[0] + '_') + filename_fragments[1]) + '_') + str((int(filename_fragments[2]) - 1))) + '_') + filename_fragments[3]) + '.png')
self.__im1.append((folder + file1))
file3 = (((((((filename_fragments[0] + '_') + filename_fragments[1]) + '_') + str((int(filename_fragments[2]) + 1))) + '_') + filename_fragments[3]) + '.png')
self.__im3.append((folder + file3))
mask_file = getMaskFileName(file)
self.__mask.append((folder + mask_file))
self.dataset_size = len(self.__file)
def __getitem__(self, index):
img1 = getImg(self.__im1[index])
img = getImg(self.__im[index])
img3 = getImg(self.__im3[index])
mask = getImg(self.__mask[index])
if (self.transform is not None):
img_tr1 = self.transform(img1)
img_tr = self.transform(img)
img_tr3 = self.transform(img3)
mask_tr = self.transform(mask)
return (img_tr1, img_tr, img_tr3, mask_tr)
def __len__(self):
return len(self.__im) |
def banner(msg: str) -> Callable:
p = (lambda s: print(s, file=sys.stderr, flush=True))
def decorate(f: Callable) -> Callable:
sig = inspect.signature(f)
C = escape_codes['bold_cyan']
R = escape_codes['bold_red']
N = escape_codes['reset']
def wrapper(*args, **kwargs):
_args = sig.bind(*args, **kwargs)
p(f'{C}:: -----BEGIN {msg}-----{N}'.format(**_args.arguments))
try:
ret = f(*args, **kwargs)
p(f'{C}:: -----END {msg}-----{N}'.format(**_args.arguments))
return ret
except BaseException as e:
p(f'{R}!! -----EXCEPTION {msg}-----{N}'.format(**_args.arguments))
raise
return wrapper
return decorate |
def _update_zipimporter_cache(normalized_path, cache, updater=None):
for p in _collect_zipimporter_cache_entries(normalized_path, cache):
old_entry = cache[p]
del cache[p]
new_entry = (updater and updater(p, old_entry))
if (new_entry is not None):
cache[p] = new_entry |
class GMMTrainer():
def __init__(self, model, dataloader_train, dataloader_val, gpu_id, log_freq, save_dir):
if torch.cuda.is_available():
self.device = torch.device(('cuda:' + str(gpu_id)))
else:
self.device = torch.device('cpu')
self.model = model.to(self.device)
self.dataloader_train = dataloader_train
self.dataloader_val = dataloader_val
self.optim = torch.optim.Adam(self.model.parameters(), lr=0.0001, betas=(0.5, 0.999))
self.criterionL1 = nn.L1Loss()
self.log_freq = log_freq
self.save_dir = save_dir
print('Total Parameters:', sum([p.nelement() for p in self.model.parameters()]))
def train(self, epoch):
return self.iteration(epoch, self.dataloader_train)
def val(self, epoch):
return self.iteration(epoch, self.dataloader_val, train=False)
def iteration(self, epoch, data_loader, train=True):
data_iter = tqdm(enumerate(data_loader), desc=('epoch: %d' % epoch), total=len(data_loader), bar_format='{l_bar}{r_bar}')
total_loss = 0.0
for (i, _data) in data_iter:
data = {}
for (key, value) in _data.items():
if (not ('name' in key)):
data[key] = value.to(self.device)
cloth = data['cloth']
person = data['person']
body_mask = data['body_mask']
(grid, _) = self.model(data['feature'], cloth)
warped_cloth = F.grid_sample(cloth, grid, padding_mode='border')
warped_grid = F.grid_sample(data['grid'], grid, padding_mode='zeros')
warped_person = ((body_mask * person) + ((1 - body_mask) * warped_cloth))
gt = ((body_mask * person) + ((1 - body_mask) * data['cloth_parse']))
visuals = [[data['head'], data['shape'], data['pose']], [cloth, warped_cloth, warped_grid], [warped_person, gt, person]]
loss = (self.criterionL1(warped_person, gt) + (0.5 * self.criterionL1(warped_cloth, data['cloth_parse'])))
if train:
self.optim.zero_grad()
loss.backward()
self.optim.step()
total_loss += loss.item()
post_fix = {'epoch': epoch, 'iter': i, 'avg_loss': (total_loss / (i + 1)), 'loss': loss.item()}
if (train and ((i % self.log_freq) == 0)):
data_iter.write(str(post_fix))
board_add_images(visuals, epoch, i, self.save_dir)
return (total_loss / len(data_iter)) |
def heatmap_viz(df: pd.DataFrame, x: str, y: str, grp_cnt_stats: Dict[(str, int)], plot_width: int, plot_height: int) -> Panel:
title = _make_title(grp_cnt_stats, x, y)
source = ColumnDataSource(data=df)
palette = RDBU[((len(RDBU) // 2) - 1):]
mapper = LinearColorMapper(palette=palette, low=(df['cnt'].min() - 0.01), high=df['cnt'].max())
if (grp_cnt_stats[f'{x}_shw'] > 60):
plot_width = (16 * grp_cnt_stats[f'{x}_shw'])
if (grp_cnt_stats[f'{y}_shw'] > 10):
plot_height = (70 + (18 * grp_cnt_stats[f'{y}_shw']))
fig = figure(x_range=sorted(list(set(df[x]))), y_range=sorted(list(set(df[y]))), toolbar_location=None, tools=[], x_axis_location='below', title=title, plot_width=plot_width, plot_height=plot_height)
renderer = fig.rect(x=x, y=y, width=1, height=1, source=source, line_color=None, fill_color=transform('cnt', mapper))
color_bar = ColorBar(color_mapper=mapper, location=(0, 0), ticker=BasicTicker(desired_num_ticks=7), formatter=PrintfTickFormatter(format='%d'))
fig.add_tools(HoverTool(tooltips=[(x, f'{{{x}}}'), (y, f'{{{y}}}'), ('Count', '')], mode='mouse', renderers=[renderer]))
fig.add_layout(color_bar, 'right')
tweak_figure(fig, 'heatmap')
fig.yaxis.formatter = FuncTickFormatter(code="\n if (tick.length > 15) return tick.substring(0, 14) + '...';\n else return tick;\n ")
return Panel(child=fig, title='Heat Map') |
_utils.in_tempdir
def test_dory_query_workflow_remove_pendants(location):
from spacegraphcats.cdbg import bcalm_to_gxt, sort_bcalm_unitigs
copy_dory_head()
copy_dory_subset()
try:
os.mkdir('dory_k21')
os.mkdir('dory_k21_r1')
except FileExistsError:
pass
args = ['-k', '21', relative_file('data/bcalm.dory.k21.unitigs.fa'), 'dory_k21/bcalm.unitigs.db', 'dory_k21/bcalm.unitigs.pickle']
assert (sort_bcalm_unitigs.main(args) == 0)
db = sqlite3.connect('dory_k21/bcalm.unitigs.db')
all_seqs = list(search_utils.contigs_iter_sqlite(db))
assert (len(all_seqs) == 736), len(all_seqs)
args = ['dory_k21/bcalm.unitigs.db', 'dory_k21/bcalm.unitigs.pickle', 'dory_k21/cdbg.gxt', 'dory_k21/contigs']
assert (bcalm_to_gxt.main(args) == 0)
db = sqlite3.connect('dory_k21/bcalm.unitigs.db')
all_seqs = list(search_utils.contigs_iter_sqlite(db))
assert (len(all_seqs) == 736), len(all_seqs)
with open('dory_k21/cdbg.gxt', 'rb') as fp:
data = fp.read()
m = hashlib.md5()
m.update(data)
assert (m.hexdigest() == '7e4d9acc9e968f7425c94f6ec78ecdd5'), m.hexdigest() |
class RandomResizedCrop(object):
def __init__(self, size, scale=(0.08, 1.0), ratio=((3.0 / 4.0), (4.0 / 3.0)), interpolation=Image.BILINEAR):
if isinstance(size, (tuple, list)):
self.size = size
else:
self.size = (size, size)
if ((scale[0] > scale[1]) or (ratio[0] > ratio[1])):
warnings.warn('range should be of kind (min, max)')
self.interpolation = interpolation
self.scale = scale
self.ratio = ratio
def get_params(img, scale, ratio):
(width, height) = _get_image_size(img)
area = (height * width)
for _ in range(10):
target_area = (random.uniform(*scale) * area)
log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
aspect_ratio = math.exp(random.uniform(*log_ratio))
w = int(round(math.sqrt((target_area * aspect_ratio))))
h = int(round(math.sqrt((target_area / aspect_ratio))))
if ((0 < w <= width) and (0 < h <= height)):
i = random.randint(0, (height - h))
j = random.randint(0, (width - w))
return (i, j, h, w)
in_ratio = (float(width) / float(height))
if (in_ratio < min(ratio)):
w = width
h = int(round((w / min(ratio))))
elif (in_ratio > max(ratio)):
h = height
w = int(round((h * max(ratio))))
else:
w = width
h = height
i = ((height - h) // 2)
j = ((width - w) // 2)
return (i, j, h, w)
def __call__(self, img, mask):
(i, j, h, w) = self.get_params(img, self.scale, self.ratio)
img = TF.resized_crop(img, i, j, h, w, self.size, self.interpolation)
mask = TF.resized_crop(mask, i, j, h, w, self.size, Image.NEAREST)
return (img, mask)
def __repr__(self):
interpolate_str = _pil_interpolation_to_str[self.interpolation]
format_string = (self.__class__.__name__ + '(size={0}'.format(self.size))
format_string += ', scale={0}'.format(tuple((round(s, 4) for s in self.scale)))
format_string += ', ratio={0}'.format(tuple((round(r, 4) for r in self.ratio)))
format_string += ', interpolation={0})'.format(interpolate_str)
return format_string |
class Logger(object):
def __init__(self, file_name: str=None, file_mode: str='w', should_flush: bool=True):
self.file = None
if (file_name is not None):
self.file = open(file_name, file_mode)
self.should_flush = should_flush
self.stdout = sys.stdout
self.stderr = sys.stderr
sys.stdout = self
sys.stderr = self
def __enter__(self) -> 'Logger':
return self
def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
self.close()
def write(self, text: Union[(str, bytes)]) -> None:
if isinstance(text, bytes):
text = text.decode()
if (len(text) == 0):
return
if (self.file is not None):
self.file.write(text)
self.stdout.write(text)
if self.should_flush:
self.flush()
def flush(self) -> None:
if (self.file is not None):
self.file.flush()
self.stdout.flush()
def close(self) -> None:
self.flush()
if (sys.stdout is self):
sys.stdout = self.stdout
if (sys.stderr is self):
sys.stderr = self.stderr
if (self.file is not None):
self.file.close()
self.file = None |
def clean_time(utter):
utter = re.sub('(\\d+) ([ap]\\.?m)', (lambda x: (x.group(1) + x.group(2))), utter)
utter = re.sub('((?<!\\d)\\d:\\d+)(am)?', '0\\1', utter)
utter = re.sub('((?<!\\d)\\d)am', '0\\1:00', utter)
utter = re.sub('((?<!\\d)\\d)pm', (lambda x: (str((int(x.group(1)) + 12)) + ':00')), utter)
utter = re.sub('(\\d+)(:\\d+)pm', (lambda x: (str((int(x.group(1)) + 12)) + x.group(2))), utter)
utter = re.sub('(\\d+)a\\.?m', '\\1', utter)
return utter |
_function_dispatch(_fft_dispatcher)
def ifft(a, n=None, axis=(- 1), norm=None):
a = asarray(a)
if (n is None):
n = a.shape[axis]
if ((norm is not None) and _unitary(norm)):
inv_norm = sqrt(max(n, 1))
else:
inv_norm = n
output = _raw_fft(a, n, axis, False, False, inv_norm)
return output |
def runNonMotifCASC(inputName, outputDir, clusters, beta, oldAssignmentsName):
if (outputDir is not None):
oldDir = ('%s/old/' % outputDir)
makeDir(oldDir)
outputDir = oldDir
return runTest(0, inputName, outputDir, clusters, beta, 1, 1, oldAssignmentsName, 15) |
class RandomWeakPushCartPole(ModifiableCartPoleEnv):
def __init__(self):
super(RandomWeakPushCartPole, self).__init__()
self.force_mag = uniform_exclude_inner(self.np_random.uniform, self.EXTREME_LOWER_FORCE_MAG, self.EXTREME_UPPER_FORCE_MAG, self.RANDOM_LOWER_FORCE_MAG, self.RANDOM_UPPER_FORCE_MAG)
def reset(self, new=True):
self.state = self.np_random.uniform(low=(- 0.05), high=0.05, size=(4,))
self.steps_beyond_done = None
if new:
self.force_mag = uniform_exclude_inner(self.np_random.uniform, self.EXTREME_LOWER_FORCE_MAG, self.EXTREME_UPPER_FORCE_MAG, self.RANDOM_LOWER_FORCE_MAG, self.RANDOM_UPPER_FORCE_MAG)
return np.array(self.state)
def parameters(self):
parameters = super(RandomWeakPushCartPole, self).parameters
parameters.update({'force': self.force_mag})
return parameters |
def register_types(module):
root_module = module.get_root()
module.add_enum('EnvironmentType', ['UrbanEnvironment', 'SubUrbanEnvironment', 'OpenAreasEnvironment'], import_from_module='ns.propagation')
module.add_enum('CitySize', ['SmallCity', 'MediumCity', 'LargeCity'], import_from_module='ns.propagation')
module.add_enum('QueueSizeUnit', ['PACKETS', 'BYTES'], import_from_module='ns.network')
module.add_enum('LogLevel', ['LOG_NONE', 'LOG_ERROR', 'LOG_LEVEL_ERROR', 'LOG_WARN', 'LOG_LEVEL_WARN', 'LOG_DEBUG', 'LOG_LEVEL_DEBUG', 'LOG_INFO', 'LOG_LEVEL_INFO', 'LOG_FUNCTION', 'LOG_LEVEL_FUNCTION', 'LOG_LOGIC', 'LOG_LEVEL_LOGIC', 'LOG_ALL', 'LOG_LEVEL_ALL', 'LOG_PREFIX_FUNC', 'LOG_PREFIX_TIME', 'LOG_PREFIX_NODE', 'LOG_PREFIX_LEVEL', 'LOG_PREFIX_ALL'], import_from_module='ns.core')
module.add_enum('MpduType', ['NORMAL_MPDU', 'MPDU_IN_AGGREGATE', 'LAST_MPDU_IN_AGGREGATE'])
module.add_enum('HtProtectionType', ['NO_PROTECTION', 'NON_MEMBER_PROTECTION', 'TWENTY_MHZ_PROTECTION', 'MIXED_MODE_PROTECTION'])
module.add_enum('TypeOfStation', ['STA', 'AP', 'ADHOC_STA', 'MESH', 'HT_STA', 'HT_AP', 'HT_ADHOC_STA', 'OCB'])
module.add_enum('WifiMacType', ['WIFI_MAC_CTL_CTLWRAPPER', 'WIFI_MAC_CTL_RTS', 'WIFI_MAC_CTL_CTS', 'WIFI_MAC_CTL_ACK', 'WIFI_MAC_CTL_BACKREQ', 'WIFI_MAC_CTL_BACKRESP', 'WIFI_MAC_CTL_END', 'WIFI_MAC_CTL_END_ACK', 'WIFI_MAC_MGT_BEACON', 'WIFI_MAC_MGT_ASSOCIATION_REQUEST', 'WIFI_MAC_MGT_ASSOCIATION_RESPONSE', 'WIFI_MAC_MGT_DISASSOCIATION', 'WIFI_MAC_MGT_REASSOCIATION_REQUEST', 'WIFI_MAC_MGT_REASSOCIATION_RESPONSE', 'WIFI_MAC_MGT_PROBE_REQUEST', 'WIFI_MAC_MGT_PROBE_RESPONSE', 'WIFI_MAC_MGT_AUTHENTICATION', 'WIFI_MAC_MGT_DEAUTHENTICATION', 'WIFI_MAC_MGT_ACTION', 'WIFI_MAC_MGT_ACTION_NO_ACK', 'WIFI_MAC_MGT_MULTIHOP_ACTION', 'WIFI_MAC_DATA', 'WIFI_MAC_DATA_CFACK', 'WIFI_MAC_DATA_CFPOLL', 'WIFI_MAC_DATA_CFACK_CFPOLL', 'WIFI_MAC_DATA_NULL', 'WIFI_MAC_DATA_NULL_CFACK', 'WIFI_MAC_DATA_NULL_CFPOLL', 'WIFI_MAC_DATA_NULL_CFACK_CFPOLL', 'WIFI_MAC_QOSDATA', 'WIFI_MAC_QOSDATA_CFACK', 'WIFI_MAC_QOSDATA_CFPOLL', 'WIFI_MAC_QOSDATA_CFACK_CFPOLL', 'WIFI_MAC_QOSDATA_NULL', 'WIFI_MAC_QOSDATA_NULL_CFPOLL', 'WIFI_MAC_QOSDATA_NULL_CFACK_CFPOLL'])
module.add_enum('AcIndex', ['AC_BE', 'AC_BK', 'AC_VI', 'AC_VO', 'AC_BE_NQOS', 'AC_UNDEF'])
module.add_enum('WifiPhyStandard', ['WIFI_PHY_STANDARD_80211a', 'WIFI_PHY_STANDARD_80211b', 'WIFI_PHY_STANDARD_80211g', 'WIFI_PHY_STANDARD_80211_10MHZ', 'WIFI_PHY_STANDARD_80211_5MHZ', 'WIFI_PHY_STANDARD_holland', 'WIFI_PHY_STANDARD_80211n_2_4GHZ', 'WIFI_PHY_STANDARD_80211n_5GHZ', 'WIFI_PHY_STANDARD_80211ac', 'WIFI_PHY_STANDARD_80211ax_2_4GHZ', 'WIFI_PHY_STANDARD_80211ax_5GHZ', 'WIFI_PHY_STANDARD_UNSPECIFIED'])
module.add_enum('WifiPreamble', ['WIFI_PREAMBLE_LONG', 'WIFI_PREAMBLE_SHORT', 'WIFI_PREAMBLE_HT_MF', 'WIFI_PREAMBLE_HT_GF', 'WIFI_PREAMBLE_VHT', 'WIFI_PREAMBLE_HE_SU', 'WIFI_PREAMBLE_HE_ER_SU', 'WIFI_PREAMBLE_HE_MU', 'WIFI_PREAMBLE_HE_TB', 'WIFI_PREAMBLE_NONE'])
module.add_enum('BlockAckType', ['BASIC_BLOCK_ACK', 'COMPRESSED_BLOCK_ACK', 'EXTENDED_COMPRESSED_BLOCK_ACK', 'MULTI_TID_BLOCK_ACK'])
module.add_enum('WifiModulationClass', ['WIFI_MOD_CLASS_UNKNOWN', 'WIFI_MOD_CLASS_IR', 'WIFI_MOD_CLASS_FHSS', 'WIFI_MOD_CLASS_DSSS', 'WIFI_MOD_CLASS_HR_DSSS', 'WIFI_MOD_CLASS_ERP_PBCC', 'WIFI_MOD_CLASS_DSSS_OFDM', 'WIFI_MOD_CLASS_ERP_OFDM', 'WIFI_MOD_CLASS_OFDM', 'WIFI_MOD_CLASS_HT', 'WIFI_MOD_CLASS_VHT', 'WIFI_MOD_CLASS_HE'])
module.add_enum('WifiCodeRate', ['WIFI_CODE_RATE_UNDEFINED', 'WIFI_CODE_RATE_3_4', 'WIFI_CODE_RATE_2_3', 'WIFI_CODE_RATE_1_2', 'WIFI_CODE_RATE_5_6'])
module.add_class('Address', import_from_module='ns.network')
module.add_enum('MaxSize_e', ['MAX_SIZE'], outer_class=root_module['ns3::Address'], import_from_module='ns.network')
module.add_class('Angles', import_from_module='ns.antenna')
module.add_class('ApInfo')
module.add_class('AsciiTraceHelper', import_from_module='ns.network')
module.add_class('AsciiTraceHelperForDevice', allow_subclassing=True, import_from_module='ns.network')
module.add_class('AthstatsHelper')
module.add_class('AttributeConstructionList', import_from_module='ns.core')
module.add_class('Item', import_from_module='ns.core', outer_class=root_module['ns3::AttributeConstructionList'])
typehandlers.add_type_alias(u'std::list< ns3::AttributeConstructionList::Item > const_iterator', u'ns3::AttributeConstructionList::CIterator')
typehandlers.add_type_alias(u'std::list< ns3::AttributeConstructionList::Item > const_iterator*', u'ns3::AttributeConstructionList::CIterator*')
typehandlers.add_type_alias(u'std::list< ns3::AttributeConstructionList::Item > const_iterator&', u'ns3::AttributeConstructionList::CIterator&')
module.add_class('BandInfo', import_from_module='ns.spectrum')
module.add_class('Bar')
module.add_class('BlockAckAgreement')
module.add_class('BlockAckCache')
module.add_class('Buffer', import_from_module='ns.network')
module.add_class('Iterator', import_from_module='ns.network', outer_class=root_module['ns3::Buffer'])
module.add_class('ByteTagIterator', import_from_module='ns.network')
module.add_class('Item', import_from_module='ns.network', outer_class=root_module['ns3::ByteTagIterator'])
module.add_class('ByteTagList', import_from_module='ns.network')
module.add_class('Iterator', import_from_module='ns.network', outer_class=root_module['ns3::ByteTagList'])
module.add_class('Item', import_from_module='ns.network', outer_class=root_module['ns3::ByteTagList::Iterator'])
module.add_class('CallbackBase', import_from_module='ns.core')
module.add_class('CapabilityInformation')
module.add_class('DataRate', import_from_module='ns.network')
module.add_class('DefaultDeleter', import_from_module='ns.core', template_parameters=['ns3::AttributeAccessor'])
module.add_class('DefaultDeleter', import_from_module='ns.core', template_parameters=['ns3::AttributeChecker'])
module.add_class('DefaultDeleter', import_from_module='ns.core', template_parameters=['ns3::AttributeValue'])
module.add_class('DefaultDeleter', import_from_module='ns.core', template_parameters=['ns3::CallbackImplBase'])
module.add_class('DefaultDeleter', template_parameters=['ns3::Event'])
module.add_class('DefaultDeleter', import_from_module='ns.core', template_parameters=['ns3::EventImpl'])
module.add_class('DefaultDeleter', import_from_module='ns.core', template_parameters=['ns3::Hash::Implementation'])
module.add_class('DefaultDeleter', template_parameters=['ns3::MacRxMiddle'])
module.add_class('DefaultDeleter', template_parameters=['ns3::MacTxMiddle'])
module.add_class('DefaultDeleter', import_from_module='ns.core', template_parameters=['ns3::NixVector'])
module.add_class('DefaultDeleter', import_from_module='ns.core', template_parameters=['ns3::OutputStreamWrapper'])
module.add_class('DefaultDeleter', import_from_module='ns.core', template_parameters=['ns3::Packet'])
module.add_class('DefaultDeleter', template_parameters=['ns3::QosBlockedDestinations'])
module.add_class('DefaultDeleter', import_from_module='ns.core', template_parameters=['ns3::QueueItem'])
module.add_class('DefaultDeleter', import_from_module='ns.core', template_parameters=['ns3::SpectrumModel'])
module.add_class('DefaultDeleter', import_from_module='ns.core', template_parameters=['ns3::SpectrumSignalParameters'])
module.add_class('DefaultDeleter', import_from_module='ns.core', template_parameters=['ns3::SpectrumValue'])
module.add_class('DefaultDeleter', import_from_module='ns.core', template_parameters=['ns3::TraceSourceAccessor'])
module.add_class('DefaultDeleter', template_parameters=['ns3::WifiInformationElement'])
module.add_class('DefaultDeleter', template_parameters=['ns3::WifiMacQueueItem'])
module.add_class('DeviceEnergyModelContainer', import_from_module='ns.energy')
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::DeviceEnergyModel > > const_iterator', u'ns3::DeviceEnergyModelContainer::Iterator')
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::DeviceEnergyModel > > const_iterator*', u'ns3::DeviceEnergyModelContainer::Iterator*')
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::DeviceEnergyModel > > const_iterator&', u'ns3::DeviceEnergyModelContainer::Iterator&')
module.add_class('DeviceEnergyModelHelper', allow_subclassing=True, import_from_module='ns.energy')
module.add_class('DsssErrorRateModel')
module.add_class('EnergySourceHelper', allow_subclassing=True, import_from_module='ns.energy')
module.add_class('EventId', import_from_module='ns.core')
module.add_class('GroupInfo')
module.add_class('Hasher', import_from_module='ns.core')
module.add_class('HePreambleParameters')
module.add_class('HtRateInfo')
module.add_class('InterferenceHelper')
module.add_class('SnrPer', outer_class=root_module['ns3::InterferenceHelper'])
module.add_class('Ipv4Address', import_from_module='ns.network')
root_module['ns3::Ipv4Address'].implicitly_converts_to(root_module['ns3::Address'])
module.add_class('Ipv4Mask', import_from_module='ns.network')
module.add_class('Ipv6Address', import_from_module='ns.network')
root_module['ns3::Ipv6Address'].implicitly_converts_to(root_module['ns3::Address'])
module.add_class('Ipv6Prefix', import_from_module='ns.network')
module.add_class('LogComponent', import_from_module='ns.core')
typehandlers.add_type_alias(u'std::map< std::string, ns3::LogComponent * >', u'ns3::LogComponent::ComponentList')
typehandlers.add_type_alias(u'std::map< std::string, ns3::LogComponent * >*', u'ns3::LogComponent::ComponentList*')
typehandlers.add_type_alias(u'std::map< std::string, ns3::LogComponent * >&', u'ns3::LogComponent::ComponentList&')
module.add_class('Mac48Address', import_from_module='ns.network')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Mac48Address )', u'ns3::Mac48Address::TracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Mac48Address )*', u'ns3::Mac48Address::TracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Mac48Address )&', u'ns3::Mac48Address::TracedCallback&')
root_module['ns3::Mac48Address'].implicitly_converts_to(root_module['ns3::Address'])
module.add_class('Mac8Address', import_from_module='ns.network')
root_module['ns3::Mac8Address'].implicitly_converts_to(root_module['ns3::Address'])
module.add_class('MacLowTransmissionParameters')
module.add_class('McsGroup')
module.add_class('MpduInfo')
module.add_class('NetDeviceContainer', import_from_module='ns.network')
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::NetDevice > > const_iterator', u'ns3::NetDeviceContainer::Iterator')
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::NetDevice > > const_iterator*', u'ns3::NetDeviceContainer::Iterator*')
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::NetDevice > > const_iterator&', u'ns3::NetDeviceContainer::Iterator&')
module.add_class('NodeContainer', import_from_module='ns.network')
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::Node > > const_iterator', u'ns3::NodeContainer::Iterator')
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::Node > > const_iterator*', u'ns3::NodeContainer::Iterator*')
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::Node > > const_iterator&', u'ns3::NodeContainer::Iterator&')
module.add_class('ObjectBase', allow_subclassing=True, import_from_module='ns.core')
module.add_class('ObjectDeleter', import_from_module='ns.core')
module.add_class('ObjectFactory', import_from_module='ns.core')
module.add_class('OriginatorBlockAckAgreement', parent=root_module['ns3::BlockAckAgreement'])
module.add_enum('State', ['PENDING', 'ESTABLISHED', 'INACTIVE', 'NO_REPLY', 'RESET', 'REJECTED'], outer_class=root_module['ns3::OriginatorBlockAckAgreement'])
module.add_class('PacketMetadata', import_from_module='ns.network')
module.add_class('Item', import_from_module='ns.network', outer_class=root_module['ns3::PacketMetadata'])
module.add_enum('ItemType', ['PAYLOAD', 'HEADER', 'TRAILER'], outer_class=root_module['ns3::PacketMetadata::Item'], import_from_module='ns.network')
module.add_class('ItemIterator', import_from_module='ns.network', outer_class=root_module['ns3::PacketMetadata'])
module.add_class('PacketTagIterator', import_from_module='ns.network')
module.add_class('Item', import_from_module='ns.network', outer_class=root_module['ns3::PacketTagIterator'])
module.add_class('PacketTagList', import_from_module='ns.network')
module.add_class('TagData', import_from_module='ns.network', outer_class=root_module['ns3::PacketTagList'])
module.add_class('ParameterLogger', import_from_module='ns.core')
module.add_class('PcapFile', import_from_module='ns.network')
module.add_class('PcapHelper', import_from_module='ns.network')
module.add_enum('DataLinkType', ['DLT_NULL', 'DLT_EN10MB', 'DLT_PPP', 'DLT_RAW', 'DLT_IEEE802_11', 'DLT_LINUX_SLL', 'DLT_PRISM_HEADER', 'DLT_IEEE802_11_RADIO', 'DLT_IEEE802_15_4', 'DLT_NETLINK'], outer_class=root_module['ns3::PcapHelper'], import_from_module='ns.network')
module.add_class('PcapHelperForDevice', allow_subclassing=True, import_from_module='ns.network')
module.add_class('PropagationCache', import_from_module='ns.propagation', template_parameters=['ns3::JakesProcess'])
module.add_class('QueueSize', import_from_module='ns.network')
module.add_class('RateInfo')
module.add_class('SignalNoiseDbm')
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::Object', 'ns3::ObjectBase', 'ns3::ObjectDeleter'], parent=root_module['ns3::ObjectBase'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('Simulator', destructor_visibility='private', import_from_module='ns.core')
module.add_enum('', ['NO_CONTEXT'], outer_class=root_module['ns3::Simulator'], import_from_module='ns.core')
module.add_class('StatusCode')
module.add_class('Tag', import_from_module='ns.network', parent=root_module['ns3::ObjectBase'])
module.add_class('TagBuffer', import_from_module='ns.network')
module.add_class('TimeWithUnit', import_from_module='ns.core')
module.add_class('TracedValue', import_from_module='ns.core', template_parameters=['double'])
module.add_class('TracedValue', import_from_module='ns.core', template_parameters=['unsigned int'])
module.add_class('TracedValue', import_from_module='ns.core', template_parameters=['unsigned long'])
module.add_class('TypeId', import_from_module='ns.core')
module.add_enum('AttributeFlag', ['ATTR_GET', 'ATTR_SET', 'ATTR_CONSTRUCT', 'ATTR_SGC'], outer_class=root_module['ns3::TypeId'], import_from_module='ns.core')
module.add_enum('SupportLevel', ['SUPPORTED', 'DEPRECATED', 'OBSOLETE'], outer_class=root_module['ns3::TypeId'], import_from_module='ns.core')
module.add_class('AttributeInformation', import_from_module='ns.core', outer_class=root_module['ns3::TypeId'])
module.add_class('TraceSourceInformation', import_from_module='ns.core', outer_class=root_module['ns3::TypeId'])
typehandlers.add_type_alias(u'uint32_t', u'ns3::TypeId::hash_t')
typehandlers.add_type_alias(u'uint32_t*', u'ns3::TypeId::hash_t*')
typehandlers.add_type_alias(u'uint32_t&', u'ns3::TypeId::hash_t&')
module.add_class('Vector2D', import_from_module='ns.core')
module.add_class('Vector3D', import_from_module='ns.core')
module.add_class('WifiHelper', allow_subclassing=True)
typehandlers.add_type_alias(u'std::function< unsigned long ( ns3::Ptr< ns3::QueueItem > ) >', u'ns3::WifiHelper::SelectQueueCallback')
typehandlers.add_type_alias(u'std::function< unsigned long ( ns3::Ptr< ns3::QueueItem > ) >*', u'ns3::WifiHelper::SelectQueueCallback*')
typehandlers.add_type_alias(u'std::function< unsigned long ( ns3::Ptr< ns3::QueueItem > ) >&', u'ns3::WifiHelper::SelectQueueCallback&')
module.add_class('WifiMacHelper', allow_subclassing=True)
module.add_class('WifiMode')
module.add_class('WifiModeFactory')
module.add_class('WifiPhyHelper', parent=[root_module['ns3::PcapHelperForDevice'], root_module['ns3::AsciiTraceHelperForDevice']])
module.add_enum('SupportedPcapDataLinkTypes', ['DLT_IEEE802_11', 'DLT_PRISM_HEADER', 'DLT_IEEE802_11_RADIO'], outer_class=root_module['ns3::WifiPhyHelper'])
module.add_class('WifiPhyListener', allow_subclassing=True)
module.add_class('WifiPhyTag', parent=root_module['ns3::Tag'])
module.add_class('WifiRadioEnergyModelHelper', parent=root_module['ns3::DeviceEnergyModelHelper'])
module.add_class('WifiRadioEnergyModelPhyListener', parent=root_module['ns3::WifiPhyListener'])
typehandlers.add_type_alias(u'ns3::Callback< void, double, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::WifiRadioEnergyModelPhyListener::UpdateTxCurrentCallback')
typehandlers.add_type_alias(u'ns3::Callback< void, double, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::WifiRadioEnergyModelPhyListener::UpdateTxCurrentCallback*')
typehandlers.add_type_alias(u'ns3::Callback< void, double, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::WifiRadioEnergyModelPhyListener::UpdateTxCurrentCallback&')
module.add_class('WifiRemoteStation')
module.add_class('WifiRemoteStationInfo')
module.add_class('WifiRemoteStationState')
module.add_enum('', ['BRAND_NEW', 'DISASSOC', 'WAIT_ASSOC_TX_OK', 'GOT_ASSOC_TX_OK'], outer_class=root_module['ns3::WifiRemoteStationState'])
module.add_class('WifiRraaThresholds')
module.add_class('WifiRrpaaThresholds')
module.add_class('WifiTxVector')
module.add_class('YansWifiChannelHelper')
module.add_class('YansWifiPhyHelper', parent=root_module['ns3::WifiPhyHelper'])
module.add_class('empty', import_from_module='ns.core')
module.add_class('int64x64_t', import_from_module='ns.core')
module.add_enum('impl_type', ['int128_impl', 'cairo_impl', 'ld_impl'], outer_class=root_module['ns3::int64x64_t'], import_from_module='ns.core')
module.add_class('AmpduTag', parent=root_module['ns3::Tag'])
module.add_class('Chunk', import_from_module='ns.network', parent=root_module['ns3::ObjectBase'])
module.add_class('Header', import_from_module='ns.network', parent=root_module['ns3::Chunk'])
module.add_class('HighLatencyCtsToSelfTxVectorTag', parent=root_module['ns3::Tag'])
module.add_class('HighLatencyDataTxVectorTag', parent=root_module['ns3::Tag'])
module.add_class('HighLatencyRtsTxVectorTag', parent=root_module['ns3::Tag'])
module.add_class('MgtAddBaRequestHeader', parent=root_module['ns3::Header'])
module.add_class('MgtAddBaResponseHeader', parent=root_module['ns3::Header'])
module.add_class('MgtAssocRequestHeader', parent=root_module['ns3::Header'])
module.add_class('MgtAssocResponseHeader', parent=root_module['ns3::Header'])
module.add_class('MgtDelBaHeader', parent=root_module['ns3::Header'])
module.add_class('MgtProbeRequestHeader', parent=root_module['ns3::Header'])
module.add_class('MgtProbeResponseHeader', parent=root_module['ns3::Header'])
module.add_class('MgtReassocRequestHeader', parent=root_module['ns3::Header'])
module.add_class('MinstrelWifiRemoteStation', parent=root_module['ns3::WifiRemoteStation'])
module.add_class('Object', import_from_module='ns.core', parent=root_module['ns3::SimpleRefCount< ns3::Object, ns3::ObjectBase, ns3::ObjectDeleter >'])
module.add_class('AggregateIterator', import_from_module='ns.core', outer_class=root_module['ns3::Object'])
module.add_class('PcapFileWrapper', import_from_module='ns.network', parent=root_module['ns3::Object'])
module.add_class('PreambleDetectionModel', parent=root_module['ns3::Object'])
module.add_class('PropagationDelayModel', import_from_module='ns.propagation', parent=root_module['ns3::Object'])
module.add_class('PropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::Object'])
module.add_class('QueueBase', import_from_module='ns.network', parent=root_module['ns3::Object'])
module.add_class('RandomPropagationDelayModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationDelayModel'])
module.add_class('RandomPropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('RandomVariableStream', import_from_module='ns.core', parent=root_module['ns3::Object'])
module.add_class('RangePropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('SequentialRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::AttributeAccessor', 'ns3::empty', 'ns3::DefaultDeleter<ns3::AttributeAccessor>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::AttributeChecker', 'ns3::empty', 'ns3::DefaultDeleter<ns3::AttributeChecker>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::AttributeValue', 'ns3::empty', 'ns3::DefaultDeleter<ns3::AttributeValue>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::CallbackImplBase', 'ns3::empty', 'ns3::DefaultDeleter<ns3::CallbackImplBase>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, template_parameters=['ns3::Event', 'ns3::empty', 'ns3::DefaultDeleter<ns3::Event>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::EventImpl', 'ns3::empty', 'ns3::DefaultDeleter<ns3::EventImpl>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::Hash::Implementation', 'ns3::empty', 'ns3::DefaultDeleter<ns3::Hash::Implementation>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, template_parameters=['ns3::MacRxMiddle', 'ns3::empty', 'ns3::DefaultDeleter<ns3::MacRxMiddle>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, template_parameters=['ns3::MacTxMiddle', 'ns3::empty', 'ns3::DefaultDeleter<ns3::MacTxMiddle>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::NixVector', 'ns3::empty', 'ns3::DefaultDeleter<ns3::NixVector>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::OutputStreamWrapper', 'ns3::empty', 'ns3::DefaultDeleter<ns3::OutputStreamWrapper>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::Packet', 'ns3::empty', 'ns3::DefaultDeleter<ns3::Packet>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, template_parameters=['ns3::QosBlockedDestinations', 'ns3::empty', 'ns3::DefaultDeleter<ns3::QosBlockedDestinations>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::QueueItem', 'ns3::empty', 'ns3::DefaultDeleter<ns3::QueueItem>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::SpectrumModel', 'ns3::empty', 'ns3::DefaultDeleter<ns3::SpectrumModel>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::SpectrumSignalParameters', 'ns3::empty', 'ns3::DefaultDeleter<ns3::SpectrumSignalParameters>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::SpectrumValue', 'ns3::empty', 'ns3::DefaultDeleter<ns3::SpectrumValue>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, import_from_module='ns.core', template_parameters=['ns3::TraceSourceAccessor', 'ns3::empty', 'ns3::DefaultDeleter<ns3::TraceSourceAccessor>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, template_parameters=['ns3::WifiInformationElement', 'ns3::empty', 'ns3::DefaultDeleter<ns3::WifiInformationElement>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SimpleRefCount', automatic_type_narrowing=True, template_parameters=['ns3::WifiMacQueueItem', 'ns3::empty', 'ns3::DefaultDeleter<ns3::WifiMacQueueItem>'], parent=root_module['ns3::empty'], memory_policy=cppclass.ReferenceCountingMethodsPolicy(incref_method='Ref', decref_method='Unref', peekref_method='GetReferenceCount'))
module.add_class('SnrTag', parent=root_module['ns3::Tag'])
module.add_class('SpectrumModel', import_from_module='ns.spectrum', parent=root_module['ns3::SimpleRefCount< ns3::SpectrumModel, ns3::empty, ns3::DefaultDeleter<ns3::SpectrumModel> >'])
module.add_class('SpectrumPhy', import_from_module='ns.spectrum', parent=root_module['ns3::Object'])
module.add_class('SpectrumPropagationLossModel', import_from_module='ns.spectrum', parent=root_module['ns3::Object'])
module.add_class('SpectrumSignalParameters', import_from_module='ns.spectrum', parent=root_module['ns3::SimpleRefCount< ns3::SpectrumSignalParameters, ns3::empty, ns3::DefaultDeleter<ns3::SpectrumSignalParameters> >'])
module.add_class('SpectrumValue', import_from_module='ns.spectrum', parent=root_module['ns3::SimpleRefCount< ns3::SpectrumValue, ns3::empty, ns3::DefaultDeleter<ns3::SpectrumValue> >'])
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::SpectrumValue > )', u'ns3::SpectrumValue::TracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::SpectrumValue > )*', u'ns3::SpectrumValue::TracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::SpectrumValue > )&', u'ns3::SpectrumValue::TracedCallback&')
module.add_class('SpectrumWifiPhyHelper', parent=root_module['ns3::WifiPhyHelper'])
module.add_class('ThreeLogDistancePropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('ThresholdPreambleDetectionModel', parent=root_module['ns3::PreambleDetectionModel'])
module.add_class('Time', import_from_module='ns.core')
module.add_enum('Unit', ['Y', 'D', 'H', 'MIN', 'S', 'MS', 'US', 'NS', 'PS', 'FS', 'LAST'], outer_class=root_module['ns3::Time'], import_from_module='ns.core')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Time )', u'ns3::Time::TracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Time )*', u'ns3::Time::TracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Time )&', u'ns3::Time::TracedCallback&')
root_module['ns3::Time'].implicitly_converts_to(root_module['ns3::int64x64_t'])
module.add_class('TraceSourceAccessor', import_from_module='ns.core', parent=root_module['ns3::SimpleRefCount< ns3::TraceSourceAccessor, ns3::empty, ns3::DefaultDeleter<ns3::TraceSourceAccessor> >'])
module.add_class('Trailer', import_from_module='ns.network', parent=root_module['ns3::Chunk'])
module.add_class('TriangularRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('TwoRayGroundPropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('Txop', parent=root_module['ns3::Object'])
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::WifiMacHeader const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::Txop::TxOk')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::WifiMacHeader const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::Txop::TxOk*')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::WifiMacHeader const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::Txop::TxOk&')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::WifiMacHeader const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::Txop::TxFailed')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::WifiMacHeader const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::Txop::TxFailed*')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::WifiMacHeader const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::Txop::TxFailed&')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet const >, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::Txop::TxDropped')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet const >, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::Txop::TxDropped*')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet const >, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::Txop::TxDropped&')
module.add_class('UniformRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('VhtConfiguration', parent=root_module['ns3::Object'])
module.add_class('WeibullRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('WifiActionHeader', parent=root_module['ns3::Header'])
module.add_enum('CategoryValue', ['BLOCK_ACK', 'MESH', 'MULTIHOP', 'SELF_PROTECTED', 'VENDOR_SPECIFIC_ACTION'], outer_class=root_module['ns3::WifiActionHeader'])
module.add_enum('SelfProtectedActionValue', ['PEER_LINK_OPEN', 'PEER_LINK_CONFIRM', 'PEER_LINK_CLOSE', 'GROUP_KEY_INFORM', 'GROUP_KEY_ACK'], outer_class=root_module['ns3::WifiActionHeader'])
module.add_enum('MultihopActionValue', ['PROXY_UPDATE', 'PROXY_UPDATE_CONFIRMATION'], outer_class=root_module['ns3::WifiActionHeader'])
module.add_enum('MeshActionValue', ['LINK_METRIC_REPORT', 'PATH_SELECTION', 'PORTAL_ANNOUNCEMENT', 'CONGESTION_CONTROL_NOTIFICATION', 'MDA_SETUP_REQUEST', 'MDA_SETUP_REPLY', 'MDAOP_ADVERTISMENT_REQUEST', 'MDAOP_ADVERTISMENTS', 'MDAOP_SET_TEARDOWN', 'TBTT_ADJUSTMENT_REQUEST', 'TBTT_ADJUSTMENT_RESPONSE'], outer_class=root_module['ns3::WifiActionHeader'])
module.add_enum('BlockAckActionValue', ['BLOCK_ACK_ADDBA_REQUEST', 'BLOCK_ACK_ADDBA_RESPONSE', 'BLOCK_ACK_DELBA'], outer_class=root_module['ns3::WifiActionHeader'])
module.add_class('ActionValue', outer_class=root_module['ns3::WifiActionHeader'])
typehandlers.add_type_alias(u'ns3::WifiActionHeader::ActionValue', u'ns3::WifiActionHeader::ActionValue')
typehandlers.add_type_alias(u'ns3::WifiActionHeader::ActionValue*', u'ns3::WifiActionHeader::ActionValue*')
typehandlers.add_type_alias(u'ns3::WifiActionHeader::ActionValue&', u'ns3::WifiActionHeader::ActionValue&')
module.add_typedef(root_module['ns3::WifiActionHeader::ActionValue'], 'ActionValue')
module.add_class('WifiInformationElement', parent=root_module['ns3::SimpleRefCount< ns3::WifiInformationElement, ns3::empty, ns3::DefaultDeleter<ns3::WifiInformationElement> >'])
module.add_class('WifiInformationElementVector', parent=root_module['ns3::Header'])
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::WifiInformationElement > > iterator', u'ns3::WifiInformationElementVector::Iterator')
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::WifiInformationElement > > iterator*', u'ns3::WifiInformationElementVector::Iterator*')
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::WifiInformationElement > > iterator&', u'ns3::WifiInformationElementVector::Iterator&')
module.add_class('WifiMac', parent=root_module['ns3::Object'])
module.add_class('WifiMacHeader', parent=root_module['ns3::Header'])
module.add_enum('QosAckPolicy', ['NORMAL_ACK', 'NO_ACK', 'NO_EXPLICIT_ACK', 'BLOCK_ACK'], outer_class=root_module['ns3::WifiMacHeader'])
module.add_enum('AddressType', ['ADDR1', 'ADDR2', 'ADDR3', 'ADDR4'], outer_class=root_module['ns3::WifiMacHeader'])
typehandlers.add_type_alias(u'void ( * ) ( ns3::WifiMacHeader const & )', u'ns3::WifiMacHeader::TracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::WifiMacHeader const & )*', u'ns3::WifiMacHeader::TracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::WifiMacHeader const & )&', u'ns3::WifiMacHeader::TracedCallback&')
module.add_class('WifiMacQueueItem', parent=root_module['ns3::SimpleRefCount< ns3::WifiMacQueueItem, ns3::empty, ns3::DefaultDeleter<ns3::WifiMacQueueItem> >'])
module.add_class('WifiMacTrailer', parent=root_module['ns3::Trailer'])
module.add_class('WifiPhy', parent=root_module['ns3::Object'])
typehandlers.add_type_alias(u'std::pair< unsigned char, ns3::WifiPhyStandard >', u'ns3::WifiPhy::ChannelNumberStandardPair')
typehandlers.add_type_alias(u'std::pair< unsigned char, ns3::WifiPhyStandard >*', u'ns3::WifiPhy::ChannelNumberStandardPair*')
typehandlers.add_type_alias(u'std::pair< unsigned char, ns3::WifiPhyStandard >&', u'ns3::WifiPhy::ChannelNumberStandardPair&')
typehandlers.add_type_alias(u'std::pair< unsigned short, unsigned short >', u'ns3::WifiPhy::FrequencyWidthPair')
typehandlers.add_type_alias(u'std::pair< unsigned short, unsigned short >*', u'ns3::WifiPhy::FrequencyWidthPair*')
typehandlers.add_type_alias(u'std::pair< unsigned short, unsigned short >&', u'ns3::WifiPhy::FrequencyWidthPair&')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, uint16_t, ns3::WifiTxVector, ns3::MpduInfo, ns3::SignalNoiseDbm )', u'ns3::WifiPhy::MonitorSnifferRxCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, uint16_t, ns3::WifiTxVector, ns3::MpduInfo, ns3::SignalNoiseDbm )*', u'ns3::WifiPhy::MonitorSnifferRxCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, uint16_t, ns3::WifiTxVector, ns3::MpduInfo, ns3::SignalNoiseDbm )&', u'ns3::WifiPhy::MonitorSnifferRxCallback&')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const > const, uint16_t, ns3::WifiTxVector, ns3::MpduInfo )', u'ns3::WifiPhy::MonitorSnifferTxCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const > const, uint16_t, ns3::WifiTxVector, ns3::MpduInfo )*', u'ns3::WifiPhy::MonitorSnifferTxCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const > const, uint16_t, ns3::WifiTxVector, ns3::MpduInfo )&', u'ns3::WifiPhy::MonitorSnifferTxCallback&')
typehandlers.add_type_alias(u'void ( * ) ( ns3::HePreambleParameters )', u'ns3::WifiPhy::EndOfHePreambleCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::HePreambleParameters )*', u'ns3::WifiPhy::EndOfHePreambleCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::HePreambleParameters )&', u'ns3::WifiPhy::EndOfHePreambleCallback&')
module.add_class('WifiPhyStateHelper', parent=root_module['ns3::Object'])
typehandlers.add_type_alias(u'void ( * ) ( ns3::Time, ns3::Time, WifiPhyState )', u'ns3::WifiPhyStateHelper::StateTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Time, ns3::Time, WifiPhyState )*', u'ns3::WifiPhyStateHelper::StateTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Time, ns3::Time, WifiPhyState )&', u'ns3::WifiPhyStateHelper::StateTracedCallback&')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, double, ns3::WifiMode, ns3::WifiPreamble )', u'ns3::WifiPhyStateHelper::RxOkTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, double, ns3::WifiMode, ns3::WifiPreamble )*', u'ns3::WifiPhyStateHelper::RxOkTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, double, ns3::WifiMode, ns3::WifiPreamble )&', u'ns3::WifiPhyStateHelper::RxOkTracedCallback&')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, double )', u'ns3::WifiPhyStateHelper::RxEndErrorTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, double )*', u'ns3::WifiPhyStateHelper::RxEndErrorTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, double )&', u'ns3::WifiPhyStateHelper::RxEndErrorTracedCallback&')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, ns3::WifiMode, ns3::WifiPreamble, uint8_t )', u'ns3::WifiPhyStateHelper::TxTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, ns3::WifiMode, ns3::WifiPreamble, uint8_t )*', u'ns3::WifiPhyStateHelper::TxTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, ns3::WifiMode, ns3::WifiPreamble, uint8_t )&', u'ns3::WifiPhyStateHelper::TxTracedCallback&')
module.add_class('WifiRemoteStationManager', parent=root_module['ns3::Object'])
module.add_enum('ProtectionMode', ['RTS_CTS', 'CTS_TO_SELF'], outer_class=root_module['ns3::WifiRemoteStationManager'])
typehandlers.add_type_alias(u'std::vector< ns3::WifiRemoteStation * >', u'ns3::WifiRemoteStationManager::Stations')
typehandlers.add_type_alias(u'std::vector< ns3::WifiRemoteStation * >*', u'ns3::WifiRemoteStationManager::Stations*')
typehandlers.add_type_alias(u'std::vector< ns3::WifiRemoteStation * >&', u'ns3::WifiRemoteStationManager::Stations&')
typehandlers.add_type_alias(u'std::vector< ns3::WifiRemoteStationState * >', u'ns3::WifiRemoteStationManager::StationStates')
typehandlers.add_type_alias(u'std::vector< ns3::WifiRemoteStationState * >*', u'ns3::WifiRemoteStationManager::StationStates*')
typehandlers.add_type_alias(u'std::vector< ns3::WifiRemoteStationState * >&', u'ns3::WifiRemoteStationManager::StationStates&')
typehandlers.add_type_alias(u'void ( * ) ( double, double, ns3::Mac48Address )', u'ns3::WifiRemoteStationManager::PowerChangeTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( double, double, ns3::Mac48Address )*', u'ns3::WifiRemoteStationManager::PowerChangeTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( double, double, ns3::Mac48Address )&', u'ns3::WifiRemoteStationManager::PowerChangeTracedCallback&')
typehandlers.add_type_alias(u'void ( * ) ( ns3::DataRate, ns3::DataRate, ns3::Mac48Address )', u'ns3::WifiRemoteStationManager::RateChangeTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::DataRate, ns3::DataRate, ns3::Mac48Address )*', u'ns3::WifiRemoteStationManager::RateChangeTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::DataRate, ns3::DataRate, ns3::Mac48Address )&', u'ns3::WifiRemoteStationManager::RateChangeTracedCallback&')
module.add_class('WifiSpectrumPhyInterface', parent=root_module['ns3::SpectrumPhy'])
module.add_class('WifiSpectrumSignalParameters', parent=root_module['ns3::SpectrumSignalParameters'])
module.add_class('WifiTxCurrentModel', parent=root_module['ns3::Object'])
module.add_class('YansWifiPhy', parent=root_module['ns3::WifiPhy'])
module.add_class('ZetaRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('ZipfRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('AarfWifiManager', parent=root_module['ns3::WifiRemoteStationManager'])
module.add_class('AarfcdWifiManager', parent=root_module['ns3::WifiRemoteStationManager'])
module.add_class('AmpduSubframeHeader', parent=root_module['ns3::Header'])
module.add_class('AmrrWifiManager', parent=root_module['ns3::WifiRemoteStationManager'])
module.add_class('AmsduSubframeHeader', parent=root_module['ns3::Header'])
module.add_class('AntennaModel', import_from_module='ns.antenna', parent=root_module['ns3::Object'])
module.add_class('AparfWifiManager', parent=root_module['ns3::WifiRemoteStationManager'])
module.add_enum('State', ['High', 'Low', 'Spread'], outer_class=root_module['ns3::AparfWifiManager'])
module.add_class('ArfWifiManager', parent=root_module['ns3::WifiRemoteStationManager'])
module.add_class('AthstatsWifiTraceSink', parent=root_module['ns3::Object'])
module.add_class('AttributeAccessor', import_from_module='ns.core', parent=root_module['ns3::SimpleRefCount< ns3::AttributeAccessor, ns3::empty, ns3::DefaultDeleter<ns3::AttributeAccessor> >'])
module.add_class('AttributeChecker', allow_subclassing=False, automatic_type_narrowing=True, import_from_module='ns.core', parent=root_module['ns3::SimpleRefCount< ns3::AttributeChecker, ns3::empty, ns3::DefaultDeleter<ns3::AttributeChecker> >'])
module.add_class('AttributeValue', allow_subclassing=False, automatic_type_narrowing=True, import_from_module='ns.core', parent=root_module['ns3::SimpleRefCount< ns3::AttributeValue, ns3::empty, ns3::DefaultDeleter<ns3::AttributeValue> >'])
module.add_class('BlockAckManager', parent=root_module['ns3::Object'])
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::WifiMacHeader const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::BlockAckManager::TxOk')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::WifiMacHeader const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::BlockAckManager::TxOk*')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::WifiMacHeader const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::BlockAckManager::TxOk&')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::WifiMacHeader const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::BlockAckManager::TxFailed')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::WifiMacHeader const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::BlockAckManager::TxFailed*')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::WifiMacHeader const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::BlockAckManager::TxFailed&')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Time, ns3::Mac48Address, uint8_t, ns3::OriginatorBlockAckAgreement::State )', u'ns3::BlockAckManager::AgreementStateTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Time, ns3::Mac48Address, uint8_t, ns3::OriginatorBlockAckAgreement::State )*', u'ns3::BlockAckManager::AgreementStateTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Time, ns3::Mac48Address, uint8_t, ns3::OriginatorBlockAckAgreement::State )&', u'ns3::BlockAckManager::AgreementStateTracedCallback&')
module.add_class('BooleanChecker', import_from_module='ns.core', parent=root_module['ns3::AttributeChecker'])
module.add_class('BooleanValue', import_from_module='ns.core', parent=root_module['ns3::AttributeValue'])
module.add_class('CallbackChecker', import_from_module='ns.core', parent=root_module['ns3::AttributeChecker'])
module.add_class('CallbackImplBase', import_from_module='ns.core', parent=root_module['ns3::SimpleRefCount< ns3::CallbackImplBase, ns3::empty, ns3::DefaultDeleter<ns3::CallbackImplBase> >'])
module.add_class('CallbackValue', import_from_module='ns.core', parent=root_module['ns3::AttributeValue'])
module.add_class('CaraWifiManager', parent=root_module['ns3::WifiRemoteStationManager'])
module.add_class('CfParameterSet', parent=root_module['ns3::WifiInformationElement'])
module.add_class('Channel', import_from_module='ns.network', parent=root_module['ns3::Object'])
module.add_class('ChannelAccessManager', parent=root_module['ns3::Object'])
module.add_class('ConstantRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('ConstantRateWifiManager', parent=root_module['ns3::WifiRemoteStationManager'])
module.add_class('ConstantSpeedPropagationDelayModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationDelayModel'])
module.add_class('Cost231PropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('CtrlBAckRequestHeader', parent=root_module['ns3::Header'])
module.add_class('CtrlBAckResponseHeader', parent=root_module['ns3::Header'])
module.add_class('DataRateChecker', import_from_module='ns.network', parent=root_module['ns3::AttributeChecker'])
module.add_class('DataRateValue', import_from_module='ns.network', parent=root_module['ns3::AttributeValue'])
module.add_class('DeterministicRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('DeviceEnergyModel', import_from_module='ns.energy', parent=root_module['ns3::Object'])
typehandlers.add_type_alias(u'ns3::Callback< void, int, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::DeviceEnergyModel::ChangeStateCallback')
typehandlers.add_type_alias(u'ns3::Callback< void, int, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::DeviceEnergyModel::ChangeStateCallback*')
typehandlers.add_type_alias(u'ns3::Callback< void, int, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::DeviceEnergyModel::ChangeStateCallback&')
module.add_class('DoubleValue', import_from_module='ns.core', parent=root_module['ns3::AttributeValue'])
module.add_class('DsssParameterSet', parent=root_module['ns3::WifiInformationElement'])
module.add_class('EdcaParameterSet', parent=root_module['ns3::WifiInformationElement'])
module.add_class('EmpiricalRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('EmptyAttributeAccessor', import_from_module='ns.core', parent=root_module['ns3::AttributeAccessor'])
module.add_class('EmptyAttributeChecker', import_from_module='ns.core', parent=root_module['ns3::AttributeChecker'])
module.add_class('EmptyAttributeValue', import_from_module='ns.core', parent=root_module['ns3::AttributeValue'])
module.add_class('EnergyHarvester', import_from_module='ns.energy', parent=root_module['ns3::Object'])
module.add_class('EnergySource', import_from_module='ns.energy', parent=root_module['ns3::Object'])
module.add_class('EnergySourceContainer', import_from_module='ns.energy', parent=root_module['ns3::Object'])
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::EnergySource > > const_iterator', u'ns3::EnergySourceContainer::Iterator')
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::EnergySource > > const_iterator*', u'ns3::EnergySourceContainer::Iterator*')
typehandlers.add_type_alias(u'std::vector< ns3::Ptr< ns3::EnergySource > > const_iterator&', u'ns3::EnergySourceContainer::Iterator&')
module.add_class('EnumChecker', import_from_module='ns.core', parent=root_module['ns3::AttributeChecker'])
module.add_class('EnumValue', import_from_module='ns.core', parent=root_module['ns3::AttributeValue'])
module.add_class('ErlangRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('ErpInformation', parent=root_module['ns3::WifiInformationElement'])
module.add_class('ErrorModel', import_from_module='ns.network', parent=root_module['ns3::Object'])
module.add_class('ErrorRateModel', parent=root_module['ns3::Object'])
module.add_class('Event', parent=root_module['ns3::SimpleRefCount< ns3::Event, ns3::empty, ns3::DefaultDeleter<ns3::Event> >'])
module.add_class('EventImpl', import_from_module='ns.core', parent=root_module['ns3::SimpleRefCount< ns3::EventImpl, ns3::empty, ns3::DefaultDeleter<ns3::EventImpl> >'])
module.add_class('ExponentialRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('ExtendedCapabilities', parent=root_module['ns3::WifiInformationElement'])
module.add_class('ExtendedSupportedRatesIE', parent=root_module['ns3::WifiInformationElement'])
module.add_class('FixedRssLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('FrameCaptureModel', parent=root_module['ns3::Object'])
module.add_class('FriisPropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('GammaRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('HeCapabilities', parent=root_module['ns3::WifiInformationElement'])
module.add_class('HeConfiguration', parent=root_module['ns3::Object'])
module.add_class('HeOperation', parent=root_module['ns3::WifiInformationElement'])
module.add_class('HtCapabilities', parent=root_module['ns3::WifiInformationElement'])
module.add_class('HtConfiguration', parent=root_module['ns3::Object'])
module.add_class('HtOperation', parent=root_module['ns3::WifiInformationElement'])
module.add_class('IdealWifiManager', parent=root_module['ns3::WifiRemoteStationManager'])
module.add_class('IntegerValue', import_from_module='ns.core', parent=root_module['ns3::AttributeValue'])
module.add_class('Ipv4AddressChecker', import_from_module='ns.network', parent=root_module['ns3::AttributeChecker'])
module.add_class('Ipv4AddressValue', import_from_module='ns.network', parent=root_module['ns3::AttributeValue'])
module.add_class('Ipv4MaskChecker', import_from_module='ns.network', parent=root_module['ns3::AttributeChecker'])
module.add_class('Ipv4MaskValue', import_from_module='ns.network', parent=root_module['ns3::AttributeValue'])
module.add_class('Ipv6AddressChecker', import_from_module='ns.network', parent=root_module['ns3::AttributeChecker'])
module.add_class('Ipv6AddressValue', import_from_module='ns.network', parent=root_module['ns3::AttributeValue'])
module.add_class('Ipv6PrefixChecker', import_from_module='ns.network', parent=root_module['ns3::AttributeChecker'])
module.add_class('Ipv6PrefixValue', import_from_module='ns.network', parent=root_module['ns3::AttributeValue'])
module.add_class('ItuR1411LosPropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('ItuR1411NlosOverRooftopPropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('JakesProcess', import_from_module='ns.propagation', parent=root_module['ns3::Object'])
module.add_class('JakesPropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('Kun2600MhzPropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('LinearWifiTxCurrentModel', parent=root_module['ns3::WifiTxCurrentModel'])
module.add_class('ListErrorModel', import_from_module='ns.network', parent=root_module['ns3::ErrorModel'])
module.add_class('LogDistancePropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('LogNormalRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('Mac48AddressChecker', import_from_module='ns.network', parent=root_module['ns3::AttributeChecker'])
module.add_class('Mac48AddressValue', import_from_module='ns.network', parent=root_module['ns3::AttributeValue'])
module.add_class('MacLow', parent=root_module['ns3::Object'])
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, ns3::WifiMacHeader const *, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::MacLow::MacLowRxCallback')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, ns3::WifiMacHeader const *, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::MacLow::MacLowRxCallback*')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, ns3::WifiMacHeader const *, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::MacLow::MacLowRxCallback&')
module.add_class('MacRxMiddle', parent=root_module['ns3::SimpleRefCount< ns3::MacRxMiddle, ns3::empty, ns3::DefaultDeleter<ns3::MacRxMiddle> >'])
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, ns3::WifiMacHeader const *, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::MacRxMiddle::ForwardUpCallback')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, ns3::WifiMacHeader const *, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::MacRxMiddle::ForwardUpCallback*')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, ns3::WifiMacHeader const *, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::MacRxMiddle::ForwardUpCallback&')
module.add_class('MacTxMiddle', parent=root_module['ns3::SimpleRefCount< ns3::MacTxMiddle, ns3::empty, ns3::DefaultDeleter<ns3::MacTxMiddle> >'])
module.add_class('MatrixPropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('MgtBeaconHeader', parent=root_module['ns3::MgtProbeResponseHeader'])
module.add_class('MinstrelHtWifiManager', parent=root_module['ns3::WifiRemoteStationManager'])
typehandlers.add_type_alias(u'void ( * ) ( uint64_t const, ns3::Mac48Address const )', u'ns3::MinstrelHtWifiManager::RateChangeTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( uint64_t const, ns3::Mac48Address const )*', u'ns3::MinstrelHtWifiManager::RateChangeTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( uint64_t const, ns3::Mac48Address const )&', u'ns3::MinstrelHtWifiManager::RateChangeTracedCallback&')
module.add_class('MinstrelWifiManager', parent=root_module['ns3::WifiRemoteStationManager'])
module.add_class('MobilityModel', import_from_module='ns.mobility', parent=root_module['ns3::Object'])
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::MobilityModel const > )', u'ns3::MobilityModel::TracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::MobilityModel const > )*', u'ns3::MobilityModel::TracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::MobilityModel const > )&', u'ns3::MobilityModel::TracedCallback&')
module.add_class('MpduAggregator', parent=root_module['ns3::Object'])
typehandlers.add_type_alias(u'std::list< std::pair< ns3::Ptr< ns3::Packet >, ns3::AmpduSubframeHeader > >', u'ns3::MpduAggregator::DeaggregatedMpdus')
typehandlers.add_type_alias(u'std::list< std::pair< ns3::Ptr< ns3::Packet >, ns3::AmpduSubframeHeader > >*', u'ns3::MpduAggregator::DeaggregatedMpdus*')
typehandlers.add_type_alias(u'std::list< std::pair< ns3::Ptr< ns3::Packet >, ns3::AmpduSubframeHeader > >&', u'ns3::MpduAggregator::DeaggregatedMpdus&')
typehandlers.add_type_alias(u'std::list< std::pair< ns3::Ptr< ns3::Packet >, ns3::AmpduSubframeHeader > > const_iterator', u'ns3::MpduAggregator::DeaggregatedMpdusCI')
typehandlers.add_type_alias(u'std::list< std::pair< ns3::Ptr< ns3::Packet >, ns3::AmpduSubframeHeader > > const_iterator*', u'ns3::MpduAggregator::DeaggregatedMpdusCI*')
typehandlers.add_type_alias(u'std::list< std::pair< ns3::Ptr< ns3::Packet >, ns3::AmpduSubframeHeader > > const_iterator&', u'ns3::MpduAggregator::DeaggregatedMpdusCI&')
typehandlers.add_type_alias(u'std::map< ns3::AcIndex, ns3::Ptr< ns3::QosTxop > >', u'ns3::MpduAggregator::EdcaQueues')
typehandlers.add_type_alias(u'std::map< ns3::AcIndex, ns3::Ptr< ns3::QosTxop > >*', u'ns3::MpduAggregator::EdcaQueues*')
typehandlers.add_type_alias(u'std::map< ns3::AcIndex, ns3::Ptr< ns3::QosTxop > >&', u'ns3::MpduAggregator::EdcaQueues&')
module.add_class('MsduAggregator', parent=root_module['ns3::Object'])
typehandlers.add_type_alias(u'std::list< std::pair< ns3::Ptr< ns3::Packet >, ns3::AmsduSubframeHeader > >', u'ns3::MsduAggregator::DeaggregatedMsdus')
typehandlers.add_type_alias(u'std::list< std::pair< ns3::Ptr< ns3::Packet >, ns3::AmsduSubframeHeader > >*', u'ns3::MsduAggregator::DeaggregatedMsdus*')
typehandlers.add_type_alias(u'std::list< std::pair< ns3::Ptr< ns3::Packet >, ns3::AmsduSubframeHeader > >&', u'ns3::MsduAggregator::DeaggregatedMsdus&')
typehandlers.add_type_alias(u'std::list< std::pair< ns3::Ptr< ns3::Packet >, ns3::AmsduSubframeHeader > > const_iterator', u'ns3::MsduAggregator::DeaggregatedMsdusCI')
typehandlers.add_type_alias(u'std::list< std::pair< ns3::Ptr< ns3::Packet >, ns3::AmsduSubframeHeader > > const_iterator*', u'ns3::MsduAggregator::DeaggregatedMsdusCI*')
typehandlers.add_type_alias(u'std::list< std::pair< ns3::Ptr< ns3::Packet >, ns3::AmsduSubframeHeader > > const_iterator&', u'ns3::MsduAggregator::DeaggregatedMsdusCI&')
typehandlers.add_type_alias(u'std::map< ns3::AcIndex, ns3::Ptr< ns3::QosTxop > >', u'ns3::MsduAggregator::EdcaQueues')
typehandlers.add_type_alias(u'std::map< ns3::AcIndex, ns3::Ptr< ns3::QosTxop > >*', u'ns3::MsduAggregator::EdcaQueues*')
typehandlers.add_type_alias(u'std::map< ns3::AcIndex, ns3::Ptr< ns3::QosTxop > >&', u'ns3::MsduAggregator::EdcaQueues&')
module.add_class('NakagamiPropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('NetDevice', import_from_module='ns.network', parent=root_module['ns3::Object'])
module.add_enum('PacketType', ['PACKET_HOST', 'NS3_PACKET_HOST', 'PACKET_BROADCAST', 'NS3_PACKET_BROADCAST', 'PACKET_MULTICAST', 'NS3_PACKET_MULTICAST', 'PACKET_OTHERHOST', 'NS3_PACKET_OTHERHOST'], outer_class=root_module['ns3::NetDevice'], import_from_module='ns.network')
typehandlers.add_type_alias(u'void ( * ) ( )', u'ns3::NetDevice::LinkChangeTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( )*', u'ns3::NetDevice::LinkChangeTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( )&', u'ns3::NetDevice::LinkChangeTracedCallback&')
typehandlers.add_type_alias(u'ns3::Callback< bool, ns3::Ptr< ns3::NetDevice >, ns3::Ptr< ns3::Packet const >, unsigned short, ns3::Address const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::NetDevice::ReceiveCallback')
typehandlers.add_type_alias(u'ns3::Callback< bool, ns3::Ptr< ns3::NetDevice >, ns3::Ptr< ns3::Packet const >, unsigned short, ns3::Address const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::NetDevice::ReceiveCallback*')
typehandlers.add_type_alias(u'ns3::Callback< bool, ns3::Ptr< ns3::NetDevice >, ns3::Ptr< ns3::Packet const >, unsigned short, ns3::Address const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::NetDevice::ReceiveCallback&')
typehandlers.add_type_alias(u'ns3::Callback< bool, ns3::Ptr< ns3::NetDevice >, ns3::Ptr< ns3::Packet const >, unsigned short, ns3::Address const &, ns3::Address const &, ns3::NetDevice::PacketType, ns3::empty, ns3::empty, ns3::empty >', u'ns3::NetDevice::PromiscReceiveCallback')
typehandlers.add_type_alias(u'ns3::Callback< bool, ns3::Ptr< ns3::NetDevice >, ns3::Ptr< ns3::Packet const >, unsigned short, ns3::Address const &, ns3::Address const &, ns3::NetDevice::PacketType, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::NetDevice::PromiscReceiveCallback*')
typehandlers.add_type_alias(u'ns3::Callback< bool, ns3::Ptr< ns3::NetDevice >, ns3::Ptr< ns3::Packet const >, unsigned short, ns3::Address const &, ns3::Address const &, ns3::NetDevice::PacketType, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::NetDevice::PromiscReceiveCallback&')
module.add_class('NistErrorRateModel', parent=root_module['ns3::ErrorRateModel'])
module.add_class('NixVector', import_from_module='ns.network', parent=root_module['ns3::SimpleRefCount< ns3::NixVector, ns3::empty, ns3::DefaultDeleter<ns3::NixVector> >'])
module.add_class('Node', import_from_module='ns.network', parent=root_module['ns3::Object'])
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::NetDevice >, ns3::Ptr< ns3::Packet const >, unsigned short, ns3::Address const &, ns3::Address const &, ns3::NetDevice::PacketType, ns3::empty, ns3::empty, ns3::empty >', u'ns3::Node::ProtocolHandler')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::NetDevice >, ns3::Ptr< ns3::Packet const >, unsigned short, ns3::Address const &, ns3::Address const &, ns3::NetDevice::PacketType, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::Node::ProtocolHandler*')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::NetDevice >, ns3::Ptr< ns3::Packet const >, unsigned short, ns3::Address const &, ns3::Address const &, ns3::NetDevice::PacketType, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::Node::ProtocolHandler&')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::NetDevice >, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::Node::DeviceAdditionListener')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::NetDevice >, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::Node::DeviceAdditionListener*')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::NetDevice >, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::Node::DeviceAdditionListener&')
module.add_class('NormalRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('ObjectFactoryChecker', import_from_module='ns.core', parent=root_module['ns3::AttributeChecker'])
module.add_class('ObjectFactoryValue', import_from_module='ns.core', parent=root_module['ns3::AttributeValue'])
module.add_class('OkumuraHataPropagationLossModel', import_from_module='ns.propagation', parent=root_module['ns3::PropagationLossModel'])
module.add_class('OnoeWifiManager', parent=root_module['ns3::WifiRemoteStationManager'])
module.add_class('OutputStreamWrapper', import_from_module='ns.network', parent=root_module['ns3::SimpleRefCount< ns3::OutputStreamWrapper, ns3::empty, ns3::DefaultDeleter<ns3::OutputStreamWrapper> >'])
module.add_class('Packet', import_from_module='ns.network', parent=root_module['ns3::SimpleRefCount< ns3::Packet, ns3::empty, ns3::DefaultDeleter<ns3::Packet> >'])
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const > )', u'ns3::Packet::TracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const > )*', u'ns3::Packet::TracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const > )&', u'ns3::Packet::TracedCallback&')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, ns3::Address const & )', u'ns3::Packet::AddressTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, ns3::Address const & )*', u'ns3::Packet::AddressTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, ns3::Address const & )&', u'ns3::Packet::AddressTracedCallback&')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const > const, ns3::Address const &, ns3::Address const & )', u'ns3::Packet::TwoAddressTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const > const, ns3::Address const &, ns3::Address const & )*', u'ns3::Packet::TwoAddressTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const > const, ns3::Address const &, ns3::Address const & )&', u'ns3::Packet::TwoAddressTracedCallback&')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, ns3::Mac48Address )', u'ns3::Packet::Mac48AddressTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, ns3::Mac48Address )*', u'ns3::Packet::Mac48AddressTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, ns3::Mac48Address )&', u'ns3::Packet::Mac48AddressTracedCallback&')
typehandlers.add_type_alias(u'void ( * ) ( uint32_t, uint32_t )', u'ns3::Packet::SizeTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( uint32_t, uint32_t )*', u'ns3::Packet::SizeTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( uint32_t, uint32_t )&', u'ns3::Packet::SizeTracedCallback&')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, double )', u'ns3::Packet::SinrTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, double )*', u'ns3::Packet::SinrTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::Packet const >, double )&', u'ns3::Packet::SinrTracedCallback&')
module.add_class('ParetoRandomVariable', import_from_module='ns.core', parent=root_module['ns3::RandomVariableStream'])
module.add_class('ParfWifiManager', parent=root_module['ns3::WifiRemoteStationManager'])
module.add_class('QosBlockedDestinations', parent=root_module['ns3::SimpleRefCount< ns3::QosBlockedDestinations, ns3::empty, ns3::DefaultDeleter<ns3::QosBlockedDestinations> >'])
module.add_class('QosTxop', parent=root_module['ns3::Txop'])
module.add_class('Queue', import_from_module='ns.network', template_parameters=['ns3::Packet'], parent=root_module['ns3::QueueBase'])
typehandlers.add_type_alias(u'ns3::Packet', u'ns3::Queue< ns3::Packet > ItemType')
typehandlers.add_type_alias(u'ns3::Packet*', u'ns3::Queue< ns3::Packet > ItemType*')
typehandlers.add_type_alias(u'ns3::Packet&', u'ns3::Queue< ns3::Packet > ItemType&')
module.add_typedef(root_module['ns3::Packet'], 'ItemType')
module.add_class('Queue', import_from_module='ns.network', template_parameters=['ns3::QueueDiscItem'], parent=root_module['ns3::QueueBase'])
typehandlers.add_type_alias(u'ns3::QueueDiscItem', u'ns3::Queue< ns3::QueueDiscItem > ItemType')
typehandlers.add_type_alias(u'ns3::QueueDiscItem*', u'ns3::Queue< ns3::QueueDiscItem > ItemType*')
typehandlers.add_type_alias(u'ns3::QueueDiscItem&', u'ns3::Queue< ns3::QueueDiscItem > ItemType&')
module.add_class('Queue', template_parameters=['ns3::WifiMacQueueItem'], parent=root_module['ns3::QueueBase'])
typehandlers.add_type_alias(u'ns3::WifiMacQueueItem', u'ns3::Queue< ns3::WifiMacQueueItem > ItemType')
typehandlers.add_type_alias(u'ns3::WifiMacQueueItem*', u'ns3::Queue< ns3::WifiMacQueueItem > ItemType*')
typehandlers.add_type_alias(u'ns3::WifiMacQueueItem&', u'ns3::Queue< ns3::WifiMacQueueItem > ItemType&')
module.add_typedef(root_module['ns3::WifiMacQueueItem'], 'ItemType')
module.add_class('QueueItem', import_from_module='ns.network', parent=root_module['ns3::SimpleRefCount< ns3::QueueItem, ns3::empty, ns3::DefaultDeleter<ns3::QueueItem> >'])
module.add_enum('Uint8Values', ['IP_DSFIELD'], outer_class=root_module['ns3::QueueItem'], import_from_module='ns.network')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::QueueItem const > )', u'ns3::QueueItem::TracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::QueueItem const > )*', u'ns3::QueueItem::TracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::QueueItem const > )&', u'ns3::QueueItem::TracedCallback&')
module.add_class('QueueSizeChecker', import_from_module='ns.network', parent=root_module['ns3::AttributeChecker'])
module.add_class('QueueSizeValue', import_from_module='ns.network', parent=root_module['ns3::AttributeValue'])
module.add_class('RateErrorModel', import_from_module='ns.network', parent=root_module['ns3::ErrorModel'])
module.add_enum('ErrorUnit', ['ERROR_UNIT_BIT', 'ERROR_UNIT_BYTE', 'ERROR_UNIT_PACKET'], outer_class=root_module['ns3::RateErrorModel'], import_from_module='ns.network')
module.add_class('ReceiveListErrorModel', import_from_module='ns.network', parent=root_module['ns3::ErrorModel'])
module.add_class('RegularWifiMac', parent=root_module['ns3::WifiMac'])
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, ns3::Mac48Address, ns3::Mac48Address, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::RegularWifiMac::ForwardUpCallback')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, ns3::Mac48Address, ns3::Mac48Address, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::RegularWifiMac::ForwardUpCallback*')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, ns3::Mac48Address, ns3::Mac48Address, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::RegularWifiMac::ForwardUpCallback&')
module.add_class('RraaWifiManager', parent=root_module['ns3::WifiRemoteStationManager'])
module.add_class('RrpaaWifiManager', parent=root_module['ns3::WifiRemoteStationManager'])
module.add_class('SimpleFrameCaptureModel', parent=root_module['ns3::FrameCaptureModel'])
module.add_class('SpectrumChannel', import_from_module='ns.spectrum', parent=root_module['ns3::Channel'])
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::SpectrumPhy const >, ns3::Ptr< ns3::SpectrumPhy const >, double )', u'ns3::SpectrumChannel::LossTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::SpectrumPhy const >, ns3::Ptr< ns3::SpectrumPhy const >, double )*', u'ns3::SpectrumChannel::LossTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::SpectrumPhy const >, ns3::Ptr< ns3::SpectrumPhy const >, double )&', u'ns3::SpectrumChannel::LossTracedCallback&')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::MobilityModel const >, ns3::Ptr< ns3::MobilityModel const >, double, double, double, double )', u'ns3::SpectrumChannel::GainTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::MobilityModel const >, ns3::Ptr< ns3::MobilityModel const >, double, double, double, double )*', u'ns3::SpectrumChannel::GainTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::MobilityModel const >, ns3::Ptr< ns3::MobilityModel const >, double, double, double, double )&', u'ns3::SpectrumChannel::GainTracedCallback&')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::SpectrumSignalParameters > )', u'ns3::SpectrumChannel::SignalParametersTracedCallback')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::SpectrumSignalParameters > )*', u'ns3::SpectrumChannel::SignalParametersTracedCallback*')
typehandlers.add_type_alias(u'void ( * ) ( ns3::Ptr< ns3::SpectrumSignalParameters > )&', u'ns3::SpectrumChannel::SignalParametersTracedCallback&')
module.add_class('SpectrumWifiPhy', parent=root_module['ns3::WifiPhy'])
typehandlers.add_type_alias(u'void ( * ) ( bool, uint32_t, double, ns3::Time )', u'ns3::SpectrumWifiPhy::SignalArrivalCallback')
typehandlers.add_type_alias(u'void ( * ) ( bool, uint32_t, double, ns3::Time )*', u'ns3::SpectrumWifiPhy::SignalArrivalCallback*')
typehandlers.add_type_alias(u'void ( * ) ( bool, uint32_t, double, ns3::Time )&', u'ns3::SpectrumWifiPhy::SignalArrivalCallback&')
module.add_class('Ssid', parent=root_module['ns3::WifiInformationElement'])
module.add_class('SsidChecker', parent=root_module['ns3::AttributeChecker'])
module.add_class('SsidValue', parent=root_module['ns3::AttributeValue'])
module.add_class('SupportedRates', parent=root_module['ns3::WifiInformationElement'])
module.add_class('TimeValue', import_from_module='ns.core', parent=root_module['ns3::AttributeValue'])
module.add_class('TypeIdChecker', import_from_module='ns.core', parent=root_module['ns3::AttributeChecker'])
module.add_class('TypeIdValue', import_from_module='ns.core', parent=root_module['ns3::AttributeValue'])
module.add_class('UintegerValue', import_from_module='ns.core', parent=root_module['ns3::AttributeValue'])
module.add_class('Vector2DChecker', import_from_module='ns.core', parent=root_module['ns3::AttributeChecker'])
module.add_class('Vector2DValue', import_from_module='ns.core', parent=root_module['ns3::AttributeValue'])
module.add_class('Vector3DChecker', import_from_module='ns.core', parent=root_module['ns3::AttributeChecker'])
module.add_class('Vector3DValue', import_from_module='ns.core', parent=root_module['ns3::AttributeValue'])
module.add_class('VhtCapabilities', parent=root_module['ns3::WifiInformationElement'])
module.add_class('VhtOperation', parent=root_module['ns3::WifiInformationElement'])
module.add_class('WifiMacQueue', parent=root_module['ns3::Queue< ns3::WifiMacQueueItem >'])
module.add_enum('DropPolicy', ['DROP_NEWEST', 'DROP_OLDEST'], outer_class=root_module['ns3::WifiMacQueue'])
module.add_class('WifiModeChecker', parent=root_module['ns3::AttributeChecker'])
module.add_class('WifiModeValue', parent=root_module['ns3::AttributeValue'])
module.add_class('WifiNetDevice', parent=root_module['ns3::NetDevice'])
module.add_class('WifiRadioEnergyModel', parent=root_module['ns3::DeviceEnergyModel'])
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::WifiRadioEnergyModel::WifiRadioEnergyDepletionCallback')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::WifiRadioEnergyModel::WifiRadioEnergyDepletionCallback*')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::WifiRadioEnergyModel::WifiRadioEnergyDepletionCallback&')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::WifiRadioEnergyModel::WifiRadioEnergyRechargedCallback')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::WifiRadioEnergyModel::WifiRadioEnergyRechargedCallback*')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::WifiRadioEnergyModel::WifiRadioEnergyRechargedCallback&')
module.add_class('YansErrorRateModel', parent=root_module['ns3::ErrorRateModel'])
module.add_class('YansWifiChannel', parent=root_module['ns3::Channel'])
module.add_class('AddressChecker', import_from_module='ns.network', parent=root_module['ns3::AttributeChecker'])
module.add_class('AddressValue', import_from_module='ns.network', parent=root_module['ns3::AttributeValue'])
module.add_class('AdhocWifiMac', parent=root_module['ns3::RegularWifiMac'])
module.add_class('BinaryErrorModel', import_from_module='ns.network', parent=root_module['ns3::ErrorModel'])
module.add_class('BurstErrorModel', import_from_module='ns.network', parent=root_module['ns3::ErrorModel'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['ns3::ObjectBase *', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'bool', 'unsigned int', 'double', 'ns3::Time', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'const ns3::WifiMacHeader &', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'double', 'double', 'ns3::Mac48Address', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'double', 'double', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'double', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'int', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::DataRate', 'ns3::DataRate', 'ns3::Mac48Address', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', template_parameters=['void', 'ns3::HePreambleParameters', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Mac48Address', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Ptr<const ns3::MobilityModel>', 'ns3::Ptr<const ns3::MobilityModel>', 'double', 'double', 'double', 'double', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Ptr<const ns3::MobilityModel>', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', template_parameters=['void', 'ns3::Ptr<const ns3::Packet>', 'double', 'ns3::WifiMode', 'ns3::WifiPreamble', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Ptr<const ns3::Packet>', 'double', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Ptr<const ns3::Packet>', 'ns3::Mac48Address', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', template_parameters=['void', 'ns3::Ptr<const ns3::Packet>', 'ns3::WifiMode', 'ns3::WifiPreamble', 'unsigned char', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Ptr<const ns3::Packet>', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', template_parameters=['void', 'ns3::Ptr<const ns3::Packet>', 'unsigned short', 'ns3::WifiTxVector', 'ns3::MpduInfo', 'ns3::SignalNoiseDbm', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', template_parameters=['void', 'ns3::Ptr<const ns3::Packet>', 'unsigned short', 'ns3::WifiTxVector', 'ns3::MpduInfo', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Ptr<const ns3::QueueDiscItem>', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Ptr<const ns3::SpectrumPhy>', 'ns3::Ptr<const ns3::SpectrumPhy>', 'double', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Ptr<const ns3::WifiMacQueueItem>', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Ptr<ns3::NetDevice>', 'ns3::Ptr<const ns3::Packet>', 'unsigned short', 'const ns3::Address &', 'const ns3::Address &', 'ns3::NetDevice::PacketType', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Ptr<ns3::NetDevice>', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Ptr<ns3::Packet>', 'const ns3::WifiMacHeader *', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', template_parameters=['void', 'ns3::Ptr<ns3::Packet>', 'double', 'ns3::WifiTxVector', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Ptr<ns3::Packet>', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Ptr<ns3::SpectrumSignalParameters>', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', template_parameters=['void', 'ns3::Time', 'ns3::Mac48Address', 'unsigned char', 'ns3::OriginatorBlockAckAgreement::State', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', template_parameters=['void', 'ns3::Time', 'ns3::Time', 'WifiPhyState', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Time', 'ns3::Time', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::Time', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'unsigned int', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'unsigned int', 'unsigned int', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('CallbackImpl', import_from_module='ns.core', template_parameters=['void', 'unsigned long', 'unsigned long', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty', 'ns3::empty'], parent=root_module['ns3::CallbackImplBase'])
module.add_class('InfrastructureWifiMac', parent=root_module['ns3::RegularWifiMac'])
module.add_class('QueueDiscItem', import_from_module='ns.network', parent=root_module['ns3::QueueItem'])
module.add_class('StaWifiMac', parent=root_module['ns3::InfrastructureWifiMac'])
module.add_class('ApWifiMac', parent=root_module['ns3::InfrastructureWifiMac'])
module.add_container('ns3::HtMinstrelRate', 'ns3::HtRateInfo', container_type=u'vector')
module.add_container('std::map< std::string, ns3::LogComponent * >', ('std::string', 'ns3::LogComponent *'), container_type=u'map')
module.add_container('ns3::TxTime', ('ns3::WifiMode', 'ns3::Time'), container_type=u'map')
module.add_container('ns3::WifiModeList', 'ns3::WifiMode', container_type=u'vector')
module.add_container('ns3::MinstrelRate', 'ns3::RateInfo', container_type=u'vector')
module.add_container('ns3::SampleRate', 'std::vector< unsigned char >', container_type=u'vector')
module.add_container('std::vector< double >', 'double', container_type=u'vector')
module.add_container('ns3::Bands', 'ns3::BandInfo', container_type=u'vector')
module.add_container('std::vector< unsigned short >', 'short unsigned int', container_type=u'vector')
module.add_container('std::vector< ns3::WifiRemoteStation * >', 'ns3::WifiRemoteStation *', container_type=u'vector')
module.add_container('std::vector< ns3::WifiRemoteStationState * >', 'ns3::WifiRemoteStationState *', container_type=u'vector')
module.add_container('std::list< unsigned int >', 'unsigned int', container_type=u'list')
module.add_container('std::list< std::pair< ns3::Ptr< ns3::Packet >, ns3::AmpduSubframeHeader > >', 'std::pair< ns3::Ptr< ns3::Packet >, ns3::AmpduSubframeHeader >', container_type=u'list')
module.add_container('std::map< ns3::AcIndex, ns3::Ptr< ns3::QosTxop > >', ('ns3::AcIndex', 'ns3::Ptr< ns3::QosTxop >'), container_type=u'map')
module.add_container('std::vector< ns3::Ptr< ns3::WifiMacQueueItem > >', 'ns3::Ptr< ns3::WifiMacQueueItem >', container_type=u'vector')
module.add_container('ns3::MpduAggregator::DeaggregatedMpdus', 'std::pair< ns3::Ptr< ns3::Packet >, ns3::AmpduSubframeHeader >', container_type=u'list')
module.add_container('ns3::MpduAggregator::EdcaQueues', ('ns3::AcIndex', 'ns3::Ptr< ns3::QosTxop >'), container_type=u'map')
module.add_container('std::list< std::pair< ns3::Ptr< ns3::Packet >, ns3::AmsduSubframeHeader > >', 'std::pair< ns3::Ptr< ns3::Packet >, ns3::AmsduSubframeHeader >', container_type=u'list')
module.add_container('ns3::MsduAggregator::DeaggregatedMsdus', 'std::pair< ns3::Ptr< ns3::Packet >, ns3::AmsduSubframeHeader >', container_type=u'list')
module.add_container('ns3::MsduAggregator::EdcaQueues', ('ns3::AcIndex', 'ns3::Ptr< ns3::QosTxop >'), container_type=u'map')
module.add_container('std::map< ns3::Mac48Address, bool >', ('ns3::Mac48Address', 'bool'), container_type=u'map')
typehandlers.add_type_alias(u'void ( * ) ( std::ostream & )', u'ns3::TimePrinter')
typehandlers.add_type_alias(u'void ( * ) ( std::ostream & )*', u'ns3::TimePrinter*')
typehandlers.add_type_alias(u'void ( * ) ( std::ostream & )&', u'ns3::TimePrinter&')
typehandlers.add_type_alias(u'void ( * ) ( std::ostream & )', u'ns3::NodePrinter')
typehandlers.add_type_alias(u'void ( * ) ( std::ostream & )*', u'ns3::NodePrinter*')
typehandlers.add_type_alias(u'void ( * ) ( std::ostream & )&', u'ns3::NodePrinter&')
typehandlers.add_type_alias(u'std::vector< double >', u'ns3::Values')
typehandlers.add_type_alias(u'std::vector< double >*', u'ns3::Values*')
typehandlers.add_type_alias(u'std::vector< double >&', u'ns3::Values&')
typehandlers.add_type_alias(u'std::vector< ns3::BandInfo >', u'ns3::Bands')
typehandlers.add_type_alias(u'std::vector< ns3::BandInfo >*', u'ns3::Bands*')
typehandlers.add_type_alias(u'std::vector< ns3::BandInfo >&', u'ns3::Bands&')
typehandlers.add_type_alias(u'uint32_t', u'ns3::SpectrumModelUid_t')
typehandlers.add_type_alias(u'uint32_t*', u'ns3::SpectrumModelUid_t*')
typehandlers.add_type_alias(u'uint32_t&', u'ns3::SpectrumModelUid_t&')
typehandlers.add_type_alias(u'ns3::Vector3D', u'ns3::Vector')
typehandlers.add_type_alias(u'ns3::Vector3D*', u'ns3::Vector*')
typehandlers.add_type_alias(u'ns3::Vector3D&', u'ns3::Vector&')
module.add_typedef(root_module['ns3::Vector3D'], 'Vector')
typehandlers.add_type_alias(u'ns3::Vector3DValue', u'ns3::VectorValue')
typehandlers.add_type_alias(u'ns3::Vector3DValue*', u'ns3::VectorValue*')
typehandlers.add_type_alias(u'ns3::Vector3DValue&', u'ns3::VectorValue&')
module.add_typedef(root_module['ns3::Vector3DValue'], 'VectorValue')
typehandlers.add_type_alias(u'ns3::Vector3DChecker', u'ns3::VectorChecker')
typehandlers.add_type_alias(u'ns3::Vector3DChecker*', u'ns3::VectorChecker*')
typehandlers.add_type_alias(u'ns3::Vector3DChecker&', u'ns3::VectorChecker&')
module.add_typedef(root_module['ns3::Vector3DChecker'], 'VectorChecker')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, double, ns3::WifiTxVector, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::RxOkCallback')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, double, ns3::WifiTxVector, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::RxOkCallback*')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, double, ns3::WifiTxVector, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::RxOkCallback&')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', u'ns3::RxErrorCallback')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >*', u'ns3::RxErrorCallback*')
typehandlers.add_type_alias(u'ns3::Callback< void, ns3::Ptr< ns3::Packet >, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >&', u'ns3::RxErrorCallback&')
typehandlers.add_type_alias(u'std::vector< std::pair< ns3::WifiRrpaaThresholds, ns3::WifiMode > >', u'ns3::RrpaaThresholdsTable')
typehandlers.add_type_alias(u'std::vector< std::pair< ns3::WifiRrpaaThresholds, ns3::WifiMode > >*', u'ns3::RrpaaThresholdsTable*')
typehandlers.add_type_alias(u'std::vector< std::pair< ns3::WifiRrpaaThresholds, ns3::WifiMode > >&', u'ns3::RrpaaThresholdsTable&')
typehandlers.add_type_alias(u'std::vector< std::vector< double > >', u'ns3::RrpaaProbabilitiesTable')
typehandlers.add_type_alias(u'std::vector< std::vector< double > >*', u'ns3::RrpaaProbabilitiesTable*')
typehandlers.add_type_alias(u'std::vector< std::vector< double > >&', u'ns3::RrpaaProbabilitiesTable&')
typehandlers.add_type_alias(u'std::vector< std::pair< ns3::WifiRraaThresholds, ns3::WifiMode > >', u'ns3::RraaThresholdsTable')
typehandlers.add_type_alias(u'std::vector< std::pair< ns3::WifiRraaThresholds, ns3::WifiMode > >*', u'ns3::RraaThresholdsTable*')
typehandlers.add_type_alias(u'std::vector< std::pair< ns3::WifiRraaThresholds, ns3::WifiMode > >&', u'ns3::RraaThresholdsTable&')
typehandlers.add_type_alias(u'std::map< ns3::WifiMode, ns3::Time >', u'ns3::TxTime')
typehandlers.add_type_alias(u'std::map< ns3::WifiMode, ns3::Time >*', u'ns3::TxTime*')
typehandlers.add_type_alias(u'std::map< ns3::WifiMode, ns3::Time >&', u'ns3::TxTime&')
typehandlers.add_type_alias(u'std::vector< ns3::McsGroup >', u'ns3::MinstrelMcsGroups')
typehandlers.add_type_alias(u'std::vector< ns3::McsGroup >*', u'ns3::MinstrelMcsGroups*')
typehandlers.add_type_alias(u'std::vector< ns3::McsGroup >&', u'ns3::MinstrelMcsGroups&')
typehandlers.add_type_alias(u'std::vector< ns3::HtRateInfo >', u'ns3::HtMinstrelRate')
typehandlers.add_type_alias(u'std::vector< ns3::HtRateInfo >*', u'ns3::HtMinstrelRate*')
typehandlers.add_type_alias(u'std::vector< ns3::HtRateInfo >&', u'ns3::HtMinstrelRate&')
typehandlers.add_type_alias(u'std::vector< ns3::GroupInfo >', u'ns3::McsGroupData')
typehandlers.add_type_alias(u'std::vector< ns3::GroupInfo >*', u'ns3::McsGroupData*')
typehandlers.add_type_alias(u'std::vector< ns3::GroupInfo >&', u'ns3::McsGroupData&')
typehandlers.add_type_alias(u'std::vector< ns3::RateInfo >', u'ns3::MinstrelRate')
typehandlers.add_type_alias(u'std::vector< ns3::RateInfo >*', u'ns3::MinstrelRate*')
typehandlers.add_type_alias(u'std::vector< ns3::RateInfo >&', u'ns3::MinstrelRate&')
typehandlers.add_type_alias(u'std::vector< std::vector< unsigned char > >', u'ns3::SampleRate')
typehandlers.add_type_alias(u'std::vector< std::vector< unsigned char > >*', u'ns3::SampleRate*')
typehandlers.add_type_alias(u'std::vector< std::vector< unsigned char > >&', u'ns3::SampleRate&')
typehandlers.add_type_alias(u'uint8_t', u'ns3::WifiInformationElementId')
typehandlers.add_type_alias(u'uint8_t*', u'ns3::WifiInformationElementId*')
typehandlers.add_type_alias(u'uint8_t&', u'ns3::WifiInformationElementId&')
typehandlers.add_type_alias(u'std::vector< ns3::WifiMode >', u'ns3::WifiModeList')
typehandlers.add_type_alias(u'std::vector< ns3::WifiMode >*', u'ns3::WifiModeList*')
typehandlers.add_type_alias(u'std::vector< ns3::WifiMode >&', u'ns3::WifiModeList&')
typehandlers.add_type_alias(u'std::vector< ns3::WifiMode > const_iterator', u'ns3::WifiModeListIterator')
typehandlers.add_type_alias(u'std::vector< ns3::WifiMode > const_iterator*', u'ns3::WifiModeListIterator*')
typehandlers.add_type_alias(u'std::vector< ns3::WifiMode > const_iterator&', u'ns3::WifiModeListIterator&')
nested_module = module.add_cpp_namespace('FatalImpl')
register_types_ns3_FatalImpl(nested_module)
nested_module = module.add_cpp_namespace('Hash')
register_types_ns3_Hash(nested_module)
nested_module = module.add_cpp_namespace('TracedValueCallback')
register_types_ns3_TracedValueCallback(nested_module)
nested_module = module.add_cpp_namespace('internal')
register_types_ns3_internal(nested_module) |
.parametrize('ctx, func_name', ctxs)
.parametrize('seed', [313])
.parametrize('including_pad', [True, False])
.parametrize('ignore_border', [True, False])
.parametrize('channel_last', [False, True])
.parametrize('inshape, kernel, stride, pad', [((3, 4, 6), (2, 2, 2), (2, 1, 1), (1, 0, 1)), ((2, 3, 4, 6), (2, 2, 2), (1, 1, 2), (0, 1, 0)), ((2, 2, 3, 4, 6), (2, 2, 2), (2, 1, 1), (1, 0, 1)), ((2, 2, 2, 3, 4, 6), (2, 2, 2), (1, 1, 2), (0, 1, 0))])
def test_average_pooling_3d(seed, inshape, kernel, stride, pad, ignore_border, channel_last, including_pad, ctx, func_name):
from nbla_test_utils import function_tester
if (channel_last and (not func_name.endswith('Cudnn'))):
pytest.skip('Channel last is only supported in Cudnn so far')
if channel_last:
t = refs.ChannelLastToFirstTranspose(len(inshape), len(kernel))
inshape = tuple((inshape[i] for i in t.inv_axes))
rng = np.random.RandomState(seed)
inputs = [rng.randn(*inshape).astype(np.float32)]
func_args = [kernel, stride, ignore_border, pad, channel_last, including_pad]
function_tester(rng, F.average_pooling, ref_average_pooling, inputs=inputs, func_args=func_args, func_name=func_name, ctx=ctx, atol_f=1e-06, atol_b=0.01) |
def register_Ns3LteRrcSapSoundingRsUlConfigCommon_methods(root_module, cls):
cls.add_constructor([])
cls.add_constructor([param('ns3::LteRrcSap::SoundingRsUlConfigCommon const &', 'arg0')])
cls.add_instance_attribute('srsBandwidthConfig', 'uint8_t', is_const=False)
cls.add_instance_attribute('srsSubframeConfig', 'uint8_t', is_const=False)
cls.add_instance_attribute('type', 'ns3::LteRrcSap::SoundingRsUlConfigCommon::action', is_const=False)
return |
class MelspecInversion(nn.Module):
def __init__(self, n_mels: int=128, sample_rate: int=24000, win_length: int=1024, hop_length: int=256):
super().__init__()
self.n_mels = n_mels
self.sample_rate = sample_rate
self.win_length = win_length
self.hop_length = hop_length
self.melspec_layer = None
def from_pretrained(cls, pretrained_model_path, **config):
model = cls(**config)
model.load_state_dict(torch.load(pretrained_model_path, map_location='cpu'))
return model
def prepare_melspectrogram(self, audio):
if (self.melspec_layer is None):
self.melspec_layer = MelSpectrogram(n_mels=self.n_mels, sample_rate=self.sample_rate, n_fft=get_least_power2_above(self.win_length), win_length=self.win_length, hop_length=self.hop_length, f_min=0.0, f_max=(self.sample_rate / 2.0), center=True, power=2.0, mel_scale='slaney', norm='slaney', normalized=True, pad_mode='constant')
self.melspec_layer = self.melspec_layer.to(audio.device)
melspec = self.melspec_layer(audio)
melspec = (10 * torch.log10((melspec + 1e-10)))
melspec = torch.clamp(((melspec + 100) / 100), min=0.0)
return melspec |
def _transfer(func):
def wrapper(manager, *arg):
returns = []
for callback in manager.callbacks:
if callback.disabled:
continue
returns.append(getattr(callback, func.__name__)(*arg))
return returns
return wrapper |
def process_punctuation(inText):
outText = inText
for p in punct:
if ((((p + ' ') in inText) or ((' ' + p) in inText)) or (re.search(comma_strip, inText) != None)):
outText = outText.replace(p, '')
else:
outText = outText.replace(p, ' ')
outText = period_strip.sub('', outText, re.UNICODE)
return outText |
class TestExpandOp(serial.SerializedTestCase):
def _rand_shape(self, X_shape, max_length):
length = np.random.randint(max_length)
shape = np.ones(length, dtype=np.int64)
i = (len(X_shape) - 1)
for j in reversed(range(length)):
if (i >= 0):
k = np.random.choice([1, X_shape[i]])
i -= 1
else:
k = (np.random.randint(3) + 1)
shape[j] = k
return shape
def _run_expand_op_test(self, X, shape, gc, dc):
shape = np.array(shape)
op = core.CreateOperator('Expand', ['X', 'shape'], ['Y'])
def ref(X, shape):
return ((X * np.ones(abs(shape))),)
self.assertReferenceChecks(gc, op, [X, shape], ref)
self.assertDeviceChecks(dc, op, [X, shape], [0])
self.assertGradientChecks(gc, op, [X, shape], 0, [0])
(X=hu.tensor(max_dim=5, dtype=np.float32), **hu.gcs)
def test_expand_rand_shape(self, X, gc, dc):
shape = self._rand_shape(X.shape, 5)
self._run_expand_op_test(X, shape, gc, dc)
(X=st.sampled_from([np.ones([1, 3, 1]), np.ones([3, 1, 3]), np.ones([1, 3])]), **hu.gcs)
def test_expand_nonrand_shape1(self, X, gc, dc):
self._run_expand_op_test(X, [3, 1, 3], gc, dc)
self._run_expand_op_test(X, [3, (- 1), 3], gc, dc)
(X=st.sampled_from([np.ones([4, 4, 2, 1]), np.ones([1, 4, 1, 2]), np.ones([4, 1, 2])]), **hu.gcs)
(deadline=1000)
def test_expand_nonrand_shape2(self, X, gc, dc):
self._run_expand_op_test(X, [4, 1, 2, 2], gc, dc)
self._run_expand_op_test(X, [4, (- 1), 2, 2], gc, dc) |
def main():
initialize()
gui = ti.GUI('Taichi MLS-MPM-99', res=512, background_color=1126209)
while (not gui.get_event(ti.GUI.ESCAPE, ti.GUI.EXIT)):
for s in range(int((0.002 // dt))):
substep()
gui.circles(x.to_numpy(), radius=1.5, palette=[427399, , ], palette_indices=material)
gui.show() |
def render_model(verts, faces, w, h, cam, near=0.5, far=25, img=None):
rn = _create_renderer(w=w, h=h, near=near, far=far, rt=cam.rt, t=cam.t, f=cam.f, c=cam.c)
if (img is not None):
rn.background_image = ((img / 255.0) if (img.max() > 1) else img)
imtmp = simple_renderer(rn, verts, faces)
if (img is None):
imtmp = get_alpha(imtmp)
return imtmp |
class ConstantPool():
def __init__(self):
self._constants: dict[(type[ConstantTypes], OrderedSet[ConstantTypes])] = {tp_: OrderedSet() for tp_ in typing.get_args(ConstantTypes)}
def add_constant(self, constant: ConstantTypes) -> None:
self._constants[type(constant)].add(constant)
def remove_constant(self, value: ConstantTypes) -> None:
values = self._constants.get(type(value))
assert (values is not None)
values.discard(value)
def has_constant_for(self, tp_: type[T]) -> bool:
return (len(self._constants[tp_]) > 0)
def get_constant_for(self, tp_: type[T]) -> T:
return typing.cast(T, randomness.choice(tuple(self._constants[tp_])))
def get_all_constants_for(self, tp_: type[T]) -> OrderedSet[T]:
return typing.cast(OrderedSet[T], self._constants[tp_])
def __len__(self):
return sum((len(value) for value in self._constants.values())) |
def get_model_inference(parameters: Params, weights_path: str=None):
(h, w) = parameters.input_shape
c = parameters.input_channels
input_images = Input(shape=(h, w, c), name='input_images')
input_seq_len = Input(shape=[1], dtype=tf.int32, name='input_seq_length')
filename_images = Input(shape=[1], dtype=tf.string, name='filename_images')
net_output = get_crnn_output(input_images, parameters)
output_seq_len = tf.identity(input_seq_len)
filenames = tf.identity(filename_images)
model = Model(inputs=[input_images, input_seq_len, filename_images], outputs=[net_output, output_seq_len, filenames])
if weights_path:
model.load_weights(weights_path)
return model |
class BleuScorer(object):
__slots__ = ('n', 'crefs', 'ctest', '_score', '_ratio', '_testlen', '_reflen', 'special_reflen')
def copy(self):
new = BleuScorer(n=self.n)
new.ctest = copy.copy(self.ctest)
new.crefs = copy.copy(self.crefs)
new._score = None
return new
def __init__(self, test=None, refs=None, n=4, special_reflen=None):
self.n = n
self.crefs = []
self.ctest = []
self.cook_append(test, refs)
self.special_reflen = special_reflen
def cook_append(self, test, refs):
if (refs is not None):
self.crefs.append(cook_refs(refs))
if (test is not None):
cooked_test = cook_test(test, self.crefs[(- 1)][0], self.crefs[(- 1)][1])
self.ctest.append(cooked_test)
else:
self.ctest.append(None)
self._score = None
def ratio(self, option=None):
self.compute_score(option=option)
return self._ratio
def score_ratio(self, option=None):
return (self.fscore(option=option), self.ratio(option=option))
def score_ratio_str(self, option=None):
return ('%.4f (%.2f)' % self.score_ratio(option))
def reflen(self, option=None):
self.compute_score(option=option)
return self._reflen
def testlen(self, option=None):
self.compute_score(option=option)
return self._testlen
def retest(self, new_test):
if (type(new_test) is str):
new_test = [new_test]
assert (len(new_test) == len(self.crefs)), new_test
self.ctest = []
for (t, rs) in zip(new_test, self.crefs):
self.ctest.append(cook_test(t, rs[0], rs[1]))
self._score = None
return self
def rescore(self, new_test):
return self.retest(new_test).compute_score()
def size(self):
assert (len(self.crefs) == len(self.ctest)), ('refs/test mismatch! %d<>%d' % (len(self.crefs), len(self.ctest)))
return len(self.crefs)
def __iadd__(self, other):
if (type(other) is tuple):
self.cook_append(other[0], other[1])
else:
assert self.compatible(other), 'incompatible BLEUs.'
self.ctest.extend(other.ctest)
self.crefs.extend(other.crefs)
self._score = None
return self
def compatible(self, other):
return (isinstance(other, BleuScorer) and (self.n == other.n))
def single_reflen(self, option='average'):
return self._single_reflen(self.crefs[0][0], option)
def _single_reflen(self, reflens, option=None, testlen=None):
if (option == 'shortest'):
reflen = min(reflens)
elif (option == 'average'):
reflen = (float(sum(reflens)) / len(reflens))
elif (option == 'closest'):
reflen = min(((abs((l - testlen)), l) for l in reflens))[1]
else:
assert False, ('unsupported reflen option %s' % option)
return reflen
def recompute_score(self, option=None, verbose=0):
self._score = None
return self.compute_score(option, verbose)
def compute_score(self, option=None, verbose=0):
n = self.n
small = 1e-09
tiny = 1e-15
bleu_list = [[] for _ in range(n)]
if (self._score is not None):
return self._score
if (option is None):
option = ('average' if (len(self.crefs) == 1) else 'closest')
self._testlen = 0
self._reflen = 0
totalcomps = {'testlen': 0, 'reflen': 0, 'guess': ([0] * n), 'correct': ([0] * n)}
for comps in self.ctest:
testlen = comps['testlen']
self._testlen += testlen
if (self.special_reflen is None):
reflen = self._single_reflen(comps['reflen'], option, testlen)
else:
reflen = self.special_reflen
self._reflen += reflen
for key in ['guess', 'correct']:
for k in range(n):
totalcomps[key][k] += comps[key][k]
bleu = 1.0
for k in range(n):
bleu *= ((float(comps['correct'][k]) + tiny) / (float(comps['guess'][k]) + small))
bleu_list[k].append((bleu ** (1.0 / (k + 1))))
ratio = ((testlen + tiny) / (reflen + small))
if (ratio < 1):
for k in range(n):
bleu_list[k][(- 1)] *= math.exp((1 - (1 / ratio)))
if (verbose > 1):
print(comps, reflen)
totalcomps['reflen'] = self._reflen
totalcomps['testlen'] = self._testlen
bleus = []
bleu = 1.0
for k in range(n):
bleu *= (float((totalcomps['correct'][k] + tiny)) / (totalcomps['guess'][k] + small))
bleus.append((bleu ** (1.0 / (k + 1))))
ratio = ((self._testlen + tiny) / (self._reflen + small))
if (ratio < 1):
for k in range(n):
bleus[k] *= math.exp((1 - (1 / ratio)))
if (verbose > 0):
print(totalcomps)
print('ratio:', ratio)
self._score = bleus
return (self._score, bleu_list) |
def main(_):
_logger = logging.getLogger('tensorflow')
_logger.setLevel('INFO')
tf_compat.v1.logging.info(('%s startup. TF version: %s' % (__file__, tf.__version__)))
if FLAGS.checkpoints:
checkpoints = [c.strip() for c in FLAGS.checkpoints.split(',')]
checkpoints = [c for c in checkpoints if c]
if (not checkpoints):
raise ValueError('No checkpoints provided for averaging.')
if FLAGS.prefix:
checkpoints = [(FLAGS.prefix + c) for c in checkpoints]
else:
assert (FLAGS.num_last_checkpoints >= 1), 'Must average at least one model'
assert FLAGS.prefix, 'Prefix must be provided when averaging last N checkpoints'
checkpoint_state = tf.train.get_checkpoint_state(os.path.dirname(FLAGS.prefix))
checkpoints = checkpoint_state.all_model_checkpoint_paths[(- FLAGS.num_last_checkpoints):]
checkpoints = [c for c in checkpoints if checkpoint_exists(c)]
if (not checkpoints):
if FLAGS.checkpoints:
raise ValueError(('None of the provided checkpoints exist. %s' % FLAGS.checkpoints))
else:
raise ValueError(('Could not find checkpoints at %s' % os.path.dirname(FLAGS.prefix)))
tf_compat.v1.logging.info('Reading variables and averaging checkpoints:')
for c in checkpoints:
tf_compat.v1.logging.info('%s ', c)
var_list = tf.train.list_variables(checkpoints[0])
(var_values, var_dtypes) = ({}, {})
for (name, shape) in var_list:
var_values[name] = numpy.zeros(shape)
for checkpoint in checkpoints:
reader = tf.train.load_checkpoint(checkpoint)
for name in var_values:
tensor = reader.get_tensor(name)
if (not isinstance(tensor, numpy.ndarray)):
tensor = numpy.array(tensor)
assert isinstance(tensor, numpy.ndarray)
var_dtypes[name] = tensor.dtype
if isinstance(tensor.dtype, numpy.integer):
var_values[name] = tensor
else:
var_values[name] += tensor
tf_compat.v1.logging.info('Read from checkpoint %s', checkpoint)
for name in var_values:
if (not isinstance(var_dtypes[name], numpy.integer)):
var_values[name] /= len(checkpoints)
with tf_compat.v1.variable_scope(tf_compat.v1.get_variable_scope(), reuse=tf_compat.v1.AUTO_REUSE):
tf_vars = [tf_compat.v1.get_variable(v, shape=var_values[v].shape, dtype=var_dtypes[v]) for v in var_values]
placeholders = [tf_compat.v1.placeholder(v.dtype, shape=v.shape) for v in tf_vars]
assign_ops = [tf_compat.v1.assign(v, p) for (v, p) in zip(tf_vars, placeholders)]
saver = tf_compat.v1.train.Saver(tf_compat.v1.all_variables())
with tf_compat.v1.Session() as sess:
sess.run(tf_compat.v1.global_variables_initializer())
for (p, assign_op, (name, value)) in zip(placeholders, assign_ops, var_values.items()):
sess.run(assign_op, {p: value})
saver.save(sess, FLAGS.output_path)
tf_compat.v1.logging.info('Averaged checkpoints saved in %s', FLAGS.output_path) |
def test_leverage_bagging_me():
stream = ConceptDriftStream(position=500, width=100, random_state=112)
nb = NaiveBayes()
learner = LeveragingBaggingClassifier(base_estimator=nb, n_estimators=5, random_state=112, leverage_algorithm='leveraging_bag_me')
y_expected = np.asarray([0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0], dtype=np.int)
run_prequential_supervised(stream, learner, max_samples=2000, n_wait=40, y_expected=y_expected) |
def pesq_eval(predict, target):
return ((pesq(fs=16000, ref=target.numpy(), deg=predict.numpy(), mode='wb') + 0.5) / 5) |
class AttentionWeightComputation(Function):
def forward(ctx, query_batch_cnt: torch.Tensor, key_batch_cnt: torch.Tensor, index_pair_batch: torch.Tensor, index_pair: torch.Tensor, query_features: torch.Tensor, key_features: torch.Tensor):
assert query_batch_cnt.is_contiguous()
assert key_batch_cnt.is_contiguous()
assert index_pair_batch.is_contiguous()
assert index_pair.is_contiguous()
assert query_features.is_contiguous()
assert key_features.is_contiguous()
b = query_batch_cnt.shape[0]
(total_query_num, local_size) = index_pair.size()
(total_key_num, nhead, hdim) = key_features.size()
assert (total_query_num == query_features.shape[0])
output = torch.cuda.FloatTensor(total_query_num, local_size, nhead).zero_()
attention_cuda.attention_weight_computation_wrapper(b, total_query_num, local_size, total_key_num, nhead, hdim, query_batch_cnt, key_batch_cnt, index_pair_batch, index_pair, query_features, key_features, output)
ctx.for_backwards = (b, total_query_num, local_size, total_key_num, nhead, hdim, query_batch_cnt, key_batch_cnt, index_pair_batch, index_pair, query_features, key_features)
return output
def backward(ctx, grad_out: torch.Tensor):
(b, total_query_num, local_size, total_key_num, nhead, hdim, query_batch_cnt, key_batch_cnt, index_pair_batch, index_pair, query_features, key_features) = ctx.for_backwards
grad_query_features = Variable(torch.cuda.FloatTensor(total_query_num, nhead, hdim).zero_())
grad_key_features = Variable(torch.cuda.FloatTensor(total_key_num, nhead, hdim).zero_())
grad_out_data = grad_out.data.contiguous()
attention_cuda.attention_weight_computation_grad_wrapper(b, total_query_num, local_size, total_key_num, nhead, hdim, query_batch_cnt, key_batch_cnt, index_pair_batch, index_pair, query_features, key_features, grad_out_data, grad_query_features.data, grad_key_features.data)
return (None, None, None, None, grad_query_features, grad_key_features) |
class DiscreteBCQImpl(DoubleDQNImpl):
_modules: DiscreteBCQModules
_action_flexibility: float
_beta: float
def __init__(self, observation_shape: Shape, action_size: int, modules: DiscreteBCQModules, q_func_forwarder: DiscreteEnsembleQFunctionForwarder, targ_q_func_forwarder: DiscreteEnsembleQFunctionForwarder, target_update_interval: int, gamma: float, action_flexibility: float, beta: float, device: str):
super().__init__(observation_shape=observation_shape, action_size=action_size, modules=modules, q_func_forwarder=q_func_forwarder, targ_q_func_forwarder=targ_q_func_forwarder, target_update_interval=target_update_interval, gamma=gamma, device=device)
self._action_flexibility = action_flexibility
self._beta = beta
def compute_loss(self, batch: TorchMiniBatch, q_tpn: torch.Tensor) -> DiscreteBCQLoss:
td_loss = super().compute_loss(batch, q_tpn).loss
imitator_loss = compute_discrete_imitation_loss(policy=self._modules.imitator, x=batch.observations, action=batch.actions.long(), beta=self._beta)
loss = (td_loss + imitator_loss)
return DiscreteBCQLoss(loss=loss, td_loss=td_loss, imitator_loss=imitator_loss)
def inner_predict_best_action(self, x: TorchObservation) -> torch.Tensor:
dist = self._modules.imitator(x)
log_probs = F.log_softmax(dist.logits, dim=1)
ratio = (log_probs - log_probs.max(dim=1, keepdim=True).values)
mask = (ratio > math.log(self._action_flexibility)).float()
value = self._q_func_forwarder.compute_expected_q(x)
normalized_value = ((value - value.min(dim=1, keepdim=True).values) + 1e-05)
action = (normalized_value * cast(torch.Tensor, mask)).argmax(dim=1)
return action |
class RemoteFolderDataset(FolderDataset, RemoteDataset):
def __init__(self, root: Union[(str, Path)], download_and_extract: bool=False, overwrite: bool=False, cleanup: bool=False, convert: bool=False, kind: str='json', n_jobs: int=1, ignore_exceptions: bool=True, use_converted: bool=None, verbose: bool=True):
RemoteDataset.__init__(self, root, download_and_extract=download_and_extract, overwrite=overwrite, cleanup=cleanup, verbose=verbose)
FolderDataset.__init__(self, root, convert=convert, kind=kind, n_jobs=n_jobs, ignore_exceptions=ignore_exceptions, use_converted=use_converted)
def read(self, filename: str) -> Music:
raise NotImplementedError |
def main():
args = get_arg()
random.seed(RAND_SEED)
np.random.seed(RAND_SEED)
torch.manual_seed(RAND_SEED)
data = load_stage2_train_all_data(datatrack=args.datatrack, feat_type=args.feat_type)
if (args.method == 'ridge'):
model = Ridge()
elif (args.method == 'linear_svr'):
model = LinearSVR(stage='stage2')
elif (args.method == 'kernel_svr'):
model = KernelSVR(stage='stage2')
elif (args.method == 'rf'):
raise NotImplementedError()
elif (args.method == 'lightgbm'):
model = LightGBM()
elif (args.method == 'svgp'):
raise NotImplementedError()
else:
raise RuntimeError('Not supported method: "{}"'.format(args.method))
best_params = model.optimize_hp(data['X'], data['y'])
logger.info(best_params)
out_dir = ((Path('../out/ensemble-multidomain/opt_hp_stage2') / args.datatrack) / f'{args.method}-{args.feat_type}')
os.makedirs(out_dir, exist_ok=True)
with open((out_dir / 'params.json'), encoding='utf-8', mode='w') as f:
json.dump(best_params, f, ensure_ascii=False, indent=2) |
class NanDetector():
def __init__(self, model, forward=True, backward=True):
self.bhooks = []
self.fhooks = []
self.forward = forward
self.backward = backward
self.reset()
for (name, mod) in model.named_modules():
mod.__module_name = name
self.add_hooks(mod)
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
self.close()
def add_hooks(self, module):
if self.forward:
self.fhooks.append(module.register_forward_hook(self.fhook_fn))
if self.backward:
self.bhooks.append(module.register_backward_hook(self.bhook_fn))
def reset(self):
self.has_printed_f = False
self.has_printed_b = False
def _detect(self, tensor, name, backward):
err = None
if (tensor.numel() >= 2):
with torch.no_grad():
if torch.isnan(tensor).any():
err = 'NaN'
elif torch.isinf(tensor).any():
err = 'Inf'
if (err is not None):
err = f"{err} detected in output of {name}, shape: {tensor.shape}, {('backward' if backward else 'forward')}"
return err
def _apply(self, module, inp, x, backward):
if torch.is_tensor(x):
if (isinstance(inp, tuple) and (len(inp) > 0)):
inp = inp[0]
err = self._detect(x, module.__module_name, backward)
if (err is not None):
if (torch.is_tensor(inp) and (not backward)):
err += f' input max: {inp.max().item()}, input min: {inp.min().item()}'
has_printed_attr = ('has_printed_b' if backward else 'has_printed_f')
logger.warning(err)
setattr(self, has_printed_attr, True)
elif isinstance(x, dict):
for v in x.values():
self._apply(module, inp, v, backward)
elif (isinstance(x, list) or isinstance(x, tuple)):
for v in x:
self._apply(module, inp, v, backward)
def fhook_fn(self, module, inp, output):
if (not self.has_printed_f):
self._apply(module, inp, output, backward=False)
def bhook_fn(self, module, inp, output):
if (not self.has_printed_b):
self._apply(module, inp, output, backward=True)
def close(self):
for hook in (self.fhooks + self.bhooks):
hook.remove() |
def msvc_runtime_library():
ver = msvc_runtime_major()
if ver:
if (ver < 140):
return ('msvcr%i' % ver)
else:
return ('vcruntime%i' % ver)
else:
return None |
def _randomly_negate_tensor(tensor):
should_flip = tf.cast(tf.floor((tf.random.uniform([]) + 0.5)), tf.bool)
final_tensor = tf.cond(should_flip, (lambda : tensor), (lambda : (- tensor)))
return final_tensor |
def readArk(filename, limit=numpy.inf):
features = []
uttids = []
with open(filename, 'rb') as f:
while True:
try:
uttid = readString(f)
except ValueError:
break
feature = readMatrix(f)
features.append(feature)
uttids.append(uttid)
if (len(features) == limit):
break
return (features, uttids) |
def max_memory_reserved(device: Union[(Device, int)]=None) -> int:
return memory_stats(device=device)['reserved_bytes.all.peak'] |
def parse_serverdesc(args):
(path, min_time, max_time) = args
relay = next(parse_file(path, document_handler='DOCUMENT', descriptor_type='server-descriptor 1.0', validate=False))
if (relay is None):
return None
pub_ts = relay.published.replace(tzinfo=timezone.utc).timestamp()
if ((pub_ts < min_time) or (pub_ts > max_time)):
return None
if (relay.observed_bandwidth is None):
return None
advertised_bw = relay.observed_bandwidth
avg_bw = relay.average_bandwidth
bst_bw = relay.burst_bandwidth
if ((avg_bw is not None) and (avg_bw < advertised_bw)):
advertised_bw = avg_bw
if ((bst_bw is not None) and (bst_bw < advertised_bw)):
advertised_bw = bst_bw
result = {'type': 'serverdesc', 'pub_dt': relay.published, 'fprint': relay.fingerprint, 'address': relay.address, 'bw_obs': relay.observed_bandwidth, 'bw_rate': (avg_bw if (avg_bw is not None) else 0), 'bw_burst': (bst_bw if (bst_bw is not None) else 0), 'bw_adv': advertised_bw}
return result |
class _Sigma0Embedding(Morphism):
def __init__(self, domain):
Morphism.__init__(self, domain.Hom(domain._matrix_space, category=Monoids()))
def _call_(self, x):
return x.matrix()
def _richcmp_(self, other, op):
return richcmp(self.domain(), other.domain(), op) |
_task('masked_lm', dataclass=MaskedLMConfig)
class MaskedLMTask(FairseqTask):
cfg: MaskedLMConfig
def __init__(self, cfg: MaskedLMConfig, dictionary):
super().__init__(cfg)
self.dictionary = dictionary
self.mask_idx = dictionary.add_symbol('<mask>')
def setup_task(cls, cfg: MaskedLMConfig, **kwargs):
paths = utils.split_paths(cfg.data)
assert (len(paths) > 0)
dictionary = Dictionary.load(os.path.join(paths[0], 'dict.txt'))
logger.info('dictionary: {} types'.format(len(dictionary)))
return cls(cfg, dictionary)
def load_dataset(self, split, epoch=1, combine=False, **kwargs):
paths = utils.split_paths(self.cfg.data)
assert (len(paths) > 0)
data_path = paths[((epoch - 1) % len(paths))]
split_path = os.path.join(data_path, split)
dataset = data_utils.load_indexed_dataset(split_path, self.source_dictionary, combine=combine)
if (dataset is None):
raise FileNotFoundError('Dataset not found: {} ({})'.format(split, split_path))
dataset = maybe_shorten_dataset(dataset, split, self.cfg.shorten_data_split_list, self.cfg.shorten_method, self.cfg.tokens_per_sample, self.cfg.seed)
dataset = TokenBlockDataset(dataset, dataset.sizes, (self.cfg.tokens_per_sample - 1), pad=self.source_dictionary.pad(), eos=self.source_dictionary.eos(), break_mode=self.cfg.sample_break_mode)
logger.info('loaded {} blocks from: {}'.format(len(dataset), split_path))
dataset = PrependTokenDataset(dataset, self.source_dictionary.bos())
mask_whole_words = (get_whole_word_mask(self.args, self.source_dictionary) if self.cfg.mask_whole_words else None)
(src_dataset, tgt_dataset) = MaskTokensDataset.apply_mask(dataset, self.source_dictionary, pad_idx=self.source_dictionary.pad(), mask_idx=self.mask_idx, seed=self.cfg.seed, mask_prob=self.cfg.mask_prob, leave_unmasked_prob=self.cfg.leave_unmasked_prob, random_token_prob=self.cfg.random_token_prob, freq_weighted_replacement=self.cfg.freq_weighted_replacement, mask_whole_words=mask_whole_words, mask_multiple_length=self.cfg.mask_multiple_length, mask_stdev=self.cfg.mask_stdev)
with data_utils.numpy_seed(self.cfg.seed):
shuffle = np.random.permutation(len(src_dataset))
self.datasets[split] = SortDataset(NestedDictionaryDataset({'id': IdDataset(), 'net_input': {'src_tokens': RightPadDataset(src_dataset, pad_idx=self.source_dictionary.pad()), 'src_lengths': NumelDataset(src_dataset, reduce=False)}, 'target': RightPadDataset(tgt_dataset, pad_idx=self.source_dictionary.pad()), 'nsentences': NumSamplesDataset(), 'ntokens': NumelDataset(src_dataset, reduce=True)}, sizes=[src_dataset.sizes]), sort_order=[shuffle, src_dataset.sizes])
def build_dataset_for_inference(self, src_tokens, src_lengths, sort=True):
src_dataset = RightPadDataset(TokenBlockDataset(src_tokens, src_lengths, (self.cfg.tokens_per_sample - 1), pad=self.source_dictionary.pad(), eos=self.source_dictionary.eos(), break_mode='eos'), pad_idx=self.source_dictionary.pad())
src_dataset = PrependTokenDataset(src_dataset, self.source_dictionary.bos())
src_dataset = NestedDictionaryDataset({'id': IdDataset(), 'net_input': {'src_tokens': src_dataset, 'src_lengths': NumelDataset(src_dataset, reduce=False)}}, sizes=src_lengths)
if sort:
src_dataset = SortDataset(src_dataset, sort_order=[src_lengths])
return src_dataset
def source_dictionary(self):
return self.dictionary
def target_dictionary(self):
return self.dictionary |
class FlaxGPTJForCausalLM(metaclass=DummyObject):
_backends = ['flax']
def __init__(self, *args, **kwargs):
requires_backends(self, ['flax']) |
def _cleanse_included_implicit_return_none(subject_properties, statement_checked_lines, statement_slice):
if ((len(statement_slice) >= 3) and (statement_slice[(- 3)].opcode == op.LOAD_CONST) and (statement_slice[(- 3)].arg is None) and (statement_slice[(- 2)].opcode == op.RETURN_VALUE)):
if ((len(statement_slice) != 3) and (statement_slice[(- 4)].lineno != statement_slice[(- 3)].lineno)):
statement_checked_lines.remove(DynamicSlicer.get_line_id_by_instruction(statement_slice[(- 3)], subject_properties)) |
def summarize_report(current_iteration, num_updates, max_updates, meter, should_print=True, extra=None, tb_writer=None, wandb_logger=None):
if (extra is None):
extra = {}
if ((not is_main()) and (not is_xla())):
return
if (wandb_logger and ('lr' in extra)):
wandb_logger.log_metrics({'train/learning_rate': float(extra['lr'])}, commit=False)
if tb_writer:
scalar_dict = meter.get_scalar_dict()
tb_writer.add_scalars(scalar_dict, current_iteration)
if wandb_logger:
metrics = meter.get_scalar_dict()
wandb_logger.log_metrics({**metrics, 'trainer/global_step': current_iteration})
if (not should_print):
return
log_dict = {}
if ((num_updates is not None) and (max_updates is not None)):
log_dict.update({'progress': f'{num_updates}/{max_updates}'})
log_dict.update(meter.get_log_dict())
log_dict.update(extra)
log_progress(log_dict) |
def test_to():
env_names = ['CartPole-v0', 'CartPole-v1']
task_envs = [GarageEnv(env_name=name) for name in env_names]
env = MultiEnvWrapper(task_envs, sample_strategy=round_robin_strategy)
deterministic.set_seed(0)
policy = TanhGaussianMLPPolicy(env_spec=env.spec, hidden_sizes=[1, 1], hidden_nonlinearity=torch.nn.ReLU, output_nonlinearity=None, min_std=np.exp((- 20.0)), max_std=np.exp(2.0))
qf1 = ContinuousMLPQFunction(env_spec=env.spec, hidden_sizes=[1, 1], hidden_nonlinearity=F.relu)
qf2 = ContinuousMLPQFunction(env_spec=env.spec, hidden_sizes=[1, 1], hidden_nonlinearity=F.relu)
replay_buffer = PathBuffer(capacity_in_transitions=int(1000000.0))
num_tasks = 2
buffer_batch_size = 2
mtsac = MTSAC(policy=policy, qf1=qf1, qf2=qf2, gradient_steps_per_itr=150, max_path_length=150, eval_env=env, env_spec=env.spec, num_tasks=num_tasks, steps_per_epoch=5, replay_buffer=replay_buffer, min_buffer_size=1000.0, target_update_tau=0.005, discount=0.99, buffer_batch_size=buffer_batch_size)
set_gpu_mode(torch.cuda.is_available())
mtsac.to()
device = global_device()
for param in mtsac._qf1.parameters():
assert (param.device == device)
for param in mtsac._qf2.parameters():
assert (param.device == device)
for param in mtsac._qf2.parameters():
assert (param.device == device)
for param in mtsac.policy.parameters():
assert (param.device == device)
assert (mtsac._log_alpha.device == device) |
def count_lus(lus_str):
total_freq = 0
lus_bow = {}
for lu in lus_str.split(','):
try:
(lu_name, lu_freq) = lu.split(':')
lu_name = lu_name.strip()
if (' ' in lu_name):
continue
lu_freq = int(lu_freq)
lus_bow[lu_name] = lu_freq
total_freq += lu_freq
except:
print(lu)
return total_freq |
def load_mat_training_data(real_fts_dir: str, gan_fts_dir: str, num_examples: int, split: float):
real_fts_files = [os.path.join(real_fts_dir, i) for i in os.listdir(real_fts_dir) if i.endswith('.mat')]
gan_fts_files = [os.path.join(gan_fts_dir, i) for i in os.listdir(gan_fts_dir) if i.endswith('.mat')]
real_fts_files.sort()
gan_fts_files.sort()
indexes = np.random.randint(len(real_fts_dir), size=num_examples)
real_fts_files = [real_fts_files[i] for i in indexes]
gan_fts_files = [gan_fts_files[i] for i in indexes]
real_split_index = int(math.ceil((len(real_fts_files) * split)))
gan_split_index = int(math.ceil((len(gan_fts_files) * split)))
(X_train, Y_train) = ([], [])
(X_test_real, Y_test_real) = ([], [])
(X_test_gan, Y_test_gan) = ([], [])
for i in real_fts_files[:real_split_index]:
fts = list(loadmat(i)['c'][0])
X_train.append(fts)
Y_train.append(1)
for i in real_fts_files[real_split_index:]:
fts = list(loadmat(i)['c'][0])
X_test_real.append(fts)
Y_test_real.append(1)
tr_real_data_break_point = len(X_train)
for i in gan_fts_files[:gan_split_index]:
fts = list(loadmat(i)['c'][0])
X_train.append(fts)
Y_train.append(0)
for i in gan_fts_files[gan_split_index:]:
fts = list(loadmat(i)['c'][0])
X_test_gan.append(fts)
Y_test_gan.append(0)
return (X_train, Y_train, X_test_real, Y_test_real, X_test_gan, Y_test_gan, tr_real_data_break_point) |
def main():
(examples, label_list) = get_data(task=args.task, train_num_per_class=args.train_num_per_class, dev_num_per_class=args.dev_num_per_class, imbalance_rate=args.imbalance_rate, data_seed=args.data_seed)
if (args.task in ['sst-2', 'sst-5']):
classifier = Classifier(label_list=label_list, device=device)
classifier.get_optimizer(learning_rate=args.learning_rate)
else:
classifier = ImageClassifier(pretrained=args.resnet_pretrained, baseline=True)
classifier.get_optimizer(learning_rate=args.image_lr, momentum=args.image_momentum, weight_decay=args.image_weight_decay)
for split in ['train', 'dev', 'test']:
classifier.load_data(set_type=split, examples=examples[split], batch_size=args.batch_size, shuffle=(split != 'test'))
print(('=' * 60), '\n', 'Training', '\n', ('=' * 60), sep='')
(best_dev_acc, final_test_acc) = ((- 1.0), (- 1.0))
for epoch in range(args.epochs):
classifier.train_epoch()
dev_acc = classifier.evaluate('dev')
if (epoch >= args.min_epochs):
do_test = (dev_acc > best_dev_acc)
best_dev_acc = max(best_dev_acc, dev_acc)
else:
do_test = False
print('Epoch {}, Dev Acc: {:.4f}, Best Ever: {:.4f}'.format(epoch, (100.0 * dev_acc), (100.0 * best_dev_acc)))
if do_test:
final_test_acc = classifier.evaluate('test')
print('Test Acc: {:.4f}'.format((100.0 * final_test_acc)))
print('Final Dev Acc: {:.4f}, Final Test Acc: {:.4f}'.format((100.0 * best_dev_acc), (100.0 * final_test_acc))) |
_utils.test()
def test_stacked_mixed_ib_and_non_ib_inner_loops_local_variable():
x = ti.field(dtype=float, shape=(), needs_dual=True)
arr = ti.field(dtype=float, shape=2, needs_dual=True)
loss = ti.field(dtype=float, shape=(), needs_dual=True)
def stacked_mixed_ib_and_non_ib_inner_loops_local_variable():
for i in arr:
loss[None] += ti.sin(x[None])
for j in range(3):
for k in range(3):
loss[None] += (ti.sin(x[None]) + 1.0)
for j in range(3):
s = 0.0
for k in range(3):
s += (ti.sin(x[None]) + 1.0)
loss[None] += s
for j in range(3):
for k in range(3):
loss[None] += (ti.sin(x[None]) + 1.0)
x[None] = 0.0
with ti.ad.FwdMode(loss=loss, param=x):
stacked_mixed_ib_and_non_ib_inner_loops_local_variable()
assert (loss[None] == 54.0)
assert (loss.dual[None] == 56.0) |
class Settings():
def __init__(self):
self._lock = threading.Lock()
self._parent_configs = {}
self._local = threading.local()
def _get_current_config(self):
return (self._local.config_stack[(- 1)] if (hasattr(self._local, 'config_stack') and self._local.config_stack) else {})
def initialize_for_thread(self, parent_tid):
with self._lock:
parent_config = self._parent_configs.get(parent_tid)
if parent_config:
self._local.config_stack = [copy.deepcopy(parent_config)]
else:
self._local.config_stack = [{}]
def context(self, **kwargs):
current_config = copy.deepcopy(self._get_current_config())
current_config.update(kwargs)
if (not hasattr(self._local, 'config_stack')):
self._local.config_stack = []
self._local.config_stack.append(current_config)
with self._lock:
self._parent_configs[threading.get_ident()] = copy.deepcopy(current_config)
try:
(yield)
finally:
self._local.config_stack.pop()
with self._lock:
self._parent_configs.pop(threading.get_ident(), None) |
def evaluate(args, model, tokenizer, output_prediction=False):
(dataset, examples) = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
if ((not os.path.exists(args.output_dir)) and (args.local_rank in [(- 1), 0])):
os.makedirs(args.output_dir)
args.eval_batch_size = (args.per_gpu_eval_batch_size * max(1, args.n_gpu))
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=partial(disamb_collate_fn, tokenizer=tokenizer))
if ((args.n_gpu > 1) and (not isinstance(model, torch.nn.DataParallel))):
model = torch.nn.DataParallel(model)
logger.info('***** Running evaluation *****')
logger.info(' Num examples = %d', len(dataset))
logger.info(' Batch size = %d', args.eval_batch_size)
start_time = timeit.default_timer()
all_pred_indexes = []
all_labels = []
for batch in tqdm(eval_dataloader, desc='Evaluating'):
model.eval()
batch = tuple((t.to(args.device) for t in batch))
with torch.no_grad():
inputs = {'input_ids': batch[0], 'token_type_ids': batch[1], 'attention_mask': batch[2], 'sample_mask': batch[3], 'labels': batch[4]}
if (args.model_type in ['xlm', 'roberta', 'distilbert', 'camembert', 'bart']):
del inputs['token_type_ids']
logits = model(**inputs)[1]
pred_indexes = torch.argmax(logits, 1).detach().cpu()
all_pred_indexes.append(pred_indexes)
all_labels.append(batch[4].cpu())
all_pred_indexes = torch.cat(all_pred_indexes).numpy()
all_labels = torch.cat(all_labels).numpy()
acc = (np.sum((all_pred_indexes == all_labels)) / len(all_pred_indexes))
evalTime = (timeit.default_timer() - start_time)
logger.info(' Evaluation done in total %f secs (%f sec per example)', evalTime, (evalTime / len(dataset)))
coverage = coverage_evaluation(examples, dataset, all_pred_indexes)
results = {'num problem': len(all_pred_indexes), 'acc': acc, 'cov': coverage}
saving = OrderedDict([(feat.pid, pred) for (feat, pred) in zip(dataset, all_pred_indexes.tolist())])
if output_prediction:
dump_json(OrderedDict([(feat.pid, pred) for (feat, pred) in zip(dataset, all_pred_indexes.tolist())]), join(args.output_dir, 'predictions.json'))
return results |
def create_tar_command(args):
Uploader(log=log, progress=Progress()).convert(args.source, args.destination) |
class SawyerHandlePressEnv(SawyerXYZEnv):
def __init__(self):
hand_low = ((- 0.5), 0.4, 0.05)
hand_high = (0.5, 1, 0.5)
obj_low = ((- 0.1), 0.8, 0.05)
obj_high = (0.1, 0.9, 0.05)
goal_low = ((- 0.1), 0.65, 0.0399)
goal_high = (0.1, 0.75, 0.0401)
super().__init__(self.model_name, hand_low=hand_low, hand_high=hand_high)
self.init_config = {'obj_init_pos': np.array([0, 0.9, 0.05]), 'hand_init_pos': np.array((0, 0.6, 0.2))}
self.goal = np.array([0, 0.8, 0.14])
self.obj_init_pos = self.init_config['obj_init_pos']
self.hand_init_pos = self.init_config['hand_init_pos']
self._random_reset_space = Box(np.array(obj_low), np.array(obj_high))
self.goal_space = Box(np.array(goal_low), np.array(goal_high))
def model_name(self):
return full_v1_path_for('sawyer_xyz/sawyer_handle_press.xml')
_assert_task_is_set
def step(self, action):
ob = super().step(action)
(reward, reachDist, pressDist) = self.compute_reward(action, ob)
self.curr_path_length += 1
info = {'reachDist': reachDist, 'goalDist': pressDist, 'epRew': reward, 'pickRew': None, 'success': float((pressDist <= 0.04))}
return (ob, reward, False, info)
def _target_site_config(self):
return []
def _get_pos_objects(self):
return self.data.site_xpos[self.model.site_name2id('handleStart')]
def _set_obj_xyz(self, pos):
qpos = self.data.qpos.flat.copy()
qvel = self.data.qvel.flat.copy()
qpos[9] = pos
qvel[9] = 0
self.set_state(qpos, qvel)
def reset_model(self):
self._reset_hand()
self._target_pos = self.goal.copy()
self.obj_init_pos = self.init_config['obj_init_pos']
if self.random_init:
goal_pos = self._get_state_rand_vec()
self.obj_init_pos = goal_pos
button_pos = goal_pos.copy()
button_pos[1] -= 0.1
button_pos[2] += 0.09
self._target_pos = button_pos
self.sim.model.body_pos[self.model.body_name2id('box')] = self.obj_init_pos
self.sim.model.body_pos[self.model.body_name2id('handle')] = self._target_pos
self._set_obj_xyz(0)
self._target_pos = self._get_site_pos('goalPress')
self.maxDist = np.abs((self.data.site_xpos[self.model.site_name2id('handleStart')][(- 1)] - self._target_pos[(- 1)]))
self.target_reward = ((1000 * self.maxDist) + (1000 * 2))
return self._get_obs()
def _reset_hand(self):
super()._reset_hand(10)
(rightFinger, leftFinger) = (self._get_site_pos('rightEndEffector'), self._get_site_pos('leftEndEffector'))
self.init_fingerCOM = ((rightFinger + leftFinger) / 2)
self.pickCompleted = False
def compute_reward(self, actions, obs):
del actions
objPos = obs[3:6]
leftFinger = self._get_site_pos('leftEndEffector')
fingerCOM = leftFinger
pressGoal = self._target_pos[(- 1)]
pressDist = np.abs((objPos[(- 1)] - pressGoal))
reachDist = np.linalg.norm((objPos - fingerCOM))
c1 = 1000
c2 = 0.01
c3 = 0.001
if (reachDist < 0.05):
pressRew = ((1000 * (self.maxDist - pressDist)) + (c1 * (np.exp(((- (pressDist ** 2)) / c2)) + np.exp(((- (pressDist ** 2)) / c3)))))
else:
pressRew = 0
pressRew = max(pressRew, 0)
reward = ((- reachDist) + pressRew)
return [reward, reachDist, pressDist] |
def _showxv(image, title=None, **options):
from . import ImageShow
ImageShow.show(image, title, **options) |
def add_model_training_inputs(model):
logger = logging.getLogger(__name__)
logger.info('Loading dataset: {}'.format(cfg.TRAIN.DATASETS))
roidb = combined_roidb_for_training(cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
logger.info('{:d} roidb entries'.format(len(roidb)))
model_builder_wsl.add_training_inputs(model, roidb=roidb) |
def ModAbVar_ambient_jacobian(group):
try:
X = _cache[group]()
if (X is not None):
return X
except KeyError:
pass
X = ModAbVar_ambient_jacobian_class(group)
_cache[group] = weakref.ref(X)
return X |
def run_clang_tidy(options, line_filters, files):
command = [options.clang_tidy_exe, '-p', options.compile_commands_dir]
if ((not options.config_file) and os.path.exists('.clang-tidy')):
options.config_file = '.clang-tidy'
if options.config_file:
import yaml
with open(options.config_file) as config:
command += ['-config', json.dumps(yaml.load(config, Loader=yaml.FullLoader))]
command += options.extra_args
if line_filters:
command += ['-line-filter', json.dumps(line_filters)]
if options.parallel:
commands = [(list(command) + [f]) for f in files]
output = run_shell_commands_in_parallel(commands)
else:
command += files
if options.dry_run:
command = [re.sub('^([{[].*[]}])$', "'\\1'", arg) for arg in command]
return ' '.join(command)
output = run_shell_command(command)
if ((not options.keep_going) and ('[clang-diagnostic-error]' in output)):
message = 'Found clang-diagnostic-errors in clang-tidy output: {}'
raise RuntimeError(message.format(output))
return output |
def contract_mwt(infile, outfile, ignore_gapping=True):
with open(outfile, 'w') as fout:
with open(infile, 'r') as fin:
idx = 0
mwt_begin = 0
mwt_end = (- 1)
for line in fin:
line = line.strip()
if line.startswith('#'):
print(line, file=fout)
continue
elif (len(line) <= 0):
print(line, file=fout)
idx = 0
mwt_begin = 0
mwt_end = (- 1)
continue
line = line.split('\t')
if (ignore_gapping and ('.' in line[0])):
continue
idx += 1
if ('-' in line[0]):
(mwt_begin, mwt_end) = [int(x) for x in line[0].split('-')]
print('{}\t{}\t{}'.format(idx, '\t'.join(line[1:(- 1)]), ('MWT=Yes' if (line[(- 1)] == '_') else (line[(- 1)] + '|MWT=Yes'))), file=fout)
idx -= 1
elif (mwt_begin <= idx <= mwt_end):
continue
else:
print('{}\t{}'.format(idx, '\t'.join(line[1:])), file=fout) |
def _format(val: Any, output_format: str='standard', errors: str='coarse') -> Any:
val = str(val)
result: Any = []
if (val in NULL_VALUES):
return [np.nan]
if (not validate_ca_sin(val)):
if (errors == 'raise'):
raise ValueError(f'Unable to parse value {val}')
error_result = (val if (errors == 'ignore') else np.nan)
return [error_result]
if (output_format == 'compact'):
result = ([sin.compact(val)] + result)
elif (output_format == 'standard'):
result = ([sin.format(val)] + result)
return result |
class attentionNet(nn.Module):
def __init__(self, squeezeFilters=32, expandFilters=64, scailingFactor=2, numAttentionBlock=10):
super(attentionNet, self).__init__()
self.inputConv = nn.Conv2d(3, squeezeFilters, 3, 1, 1)
self.globalPooling = nn.AvgPool2d(2, 2)
depthAttenBlock = []
for i in range(numAttentionBlock):
depthAttenBlock.append(depthAttentiveResBlock(squeezeFilters, expandFilters))
self.spatialFeatExtBlock = nn.Sequential(*depthAttenBlock)
self.psUpsampling = pixelShuffleUpsampling(inputFilters=squeezeFilters, scailingFactor=2)
self.featureAttention1 = selfAttention(squeezeFilters, squeezeFilters, 3, 1, 1)
depthAttenBlock = []
for i in range((numAttentionBlock // 2)):
depthAttenBlock.append(depthAttentiveResBlock(squeezeFilters, expandFilters))
self.fullFeatCorelationBlock = nn.Sequential(*depthAttenBlock)
self.featureAttention2 = selfAttention(squeezeFilters, squeezeFilters, 3, 1, 1)
self.convOut = nn.Conv2d(squeezeFilters, 3, 1)
self._initialize_weights()
def forward(self, img):
xInp = F.relu(self.inputConv(img))
xGAP = self.globalPooling(xInp)
xSPE = self.spatialFeatExtBlock(xGAP)
xPUP = (F.relu(self.psUpsampling(xSPE)) + xInp)
xFA1 = F.relu(self.featureAttention1(xPUP))
XFFC = self.fullFeatCorelationBlock(xFA1)
xFA2 = (F.relu(self.featureAttention2(XFFC)) + xFA1)
return torch.tanh((self.convOut(xFA2) + img))
def _initialize_weights(self):
self.inputConv.apply(init_weights)
self.globalPooling.apply(init_weights)
self.spatialFeatExtBlock.apply(init_weights)
self.psUpsampling.apply(init_weights)
self.featureAttention1.apply(init_weights)
self.fullFeatCorelationBlock.apply(init_weights)
self.featureAttention2.apply(init_weights)
self.convOut.apply(init_weights) |
def register_Ns3Ipv4GlobalRouting_methods(root_module, cls):
cls.add_constructor([param('ns3::Ipv4GlobalRouting const &', 'arg0')])
cls.add_constructor([])
cls.add_method('AddASExternalRouteTo', 'void', [param('ns3::Ipv4Address', 'network'), param('ns3::Ipv4Mask', 'networkMask'), param('ns3::Ipv4Address', 'nextHop'), param('uint32_t', 'interface')])
cls.add_method('AddHostRouteTo', 'void', [param('ns3::Ipv4Address', 'dest'), param('ns3::Ipv4Address', 'nextHop'), param('uint32_t', 'interface')])
cls.add_method('AddHostRouteTo', 'void', [param('ns3::Ipv4Address', 'dest'), param('uint32_t', 'interface')])
cls.add_method('AddNetworkRouteTo', 'void', [param('ns3::Ipv4Address', 'network'), param('ns3::Ipv4Mask', 'networkMask'), param('ns3::Ipv4Address', 'nextHop'), param('uint32_t', 'interface')])
cls.add_method('AddNetworkRouteTo', 'void', [param('ns3::Ipv4Address', 'network'), param('ns3::Ipv4Mask', 'networkMask'), param('uint32_t', 'interface')])
cls.add_method('AssignStreams', 'int64_t', [param('int64_t', 'stream')])
cls.add_method('GetNRoutes', 'uint32_t', [], is_const=True)
cls.add_method('GetRoute', retval('ns3::Ipv4RoutingTableEntry *', caller_owns_return=False), [param('uint32_t', 'i')], is_const=True)
cls.add_method('GetTypeId', 'ns3::TypeId', [], is_static=True)
cls.add_method('NotifyAddAddress', 'void', [param('uint32_t', 'interface'), param('ns3::Ipv4InterfaceAddress', 'address')], is_virtual=True)
cls.add_method('NotifyInterfaceDown', 'void', [param('uint32_t', 'interface')], is_virtual=True)
cls.add_method('NotifyInterfaceUp', 'void', [param('uint32_t', 'interface')], is_virtual=True)
cls.add_method('NotifyRemoveAddress', 'void', [param('uint32_t', 'interface'), param('ns3::Ipv4InterfaceAddress', 'address')], is_virtual=True)
cls.add_method('PrintRoutingTable', 'void', [param('ns3::Ptr< ns3::OutputStreamWrapper >', 'stream'), param('ns3::Time::Unit', 'unit', default_value='::ns3::Time::Unit::S')], is_const=True, is_virtual=True)
cls.add_method('RemoveRoute', 'void', [param('uint32_t', 'i')])
cls.add_method('RouteInput', 'bool', [param('ns3::Ptr< ns3::Packet const >', 'p'), param('ns3::Ipv4Header const &', 'header'), param('ns3::Ptr< ns3::NetDevice const >', 'idev'), param('ns3::Callback< void, ns3::Ptr< ns3::Ipv4Route >, ns3::Ptr< ns3::Packet const >, ns3::Ipv4Header const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', 'ucb'), param('ns3::Callback< void, ns3::Ptr< ns3::Ipv4MulticastRoute >, ns3::Ptr< ns3::Packet const >, ns3::Ipv4Header const &, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', 'mcb'), param('ns3::Callback< void, ns3::Ptr< ns3::Packet const >, ns3::Ipv4Header const &, unsigned int, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', 'lcb'), param('ns3::Callback< void, ns3::Ptr< ns3::Packet const >, ns3::Ipv4Header const &, ns3::Socket::SocketErrno, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty, ns3::empty >', 'ecb')], is_virtual=True)
cls.add_method('RouteOutput', 'ns3::Ptr< ns3::Ipv4Route >', [param('ns3::Ptr< ns3::Packet >', 'p'), param('ns3::Ipv4Header const &', 'header'), param('ns3::Ptr< ns3::NetDevice >', 'oif'), param('ns3::Socket::SocketErrno &', 'sockerr')], is_virtual=True)
cls.add_method('SetIpv4', 'void', [param('ns3::Ptr< ns3::Ipv4 >', 'ipv4')], is_virtual=True)
cls.add_method('DoDispose', 'void', [], visibility='protected', is_virtual=True)
return |
def load_pickle_model(model_path: str) -> CRF:
with open(model_path, 'rb') as pkl_model:
model = pickle.load(pkl_model)
return model |
def read_sentences(filename, encoding):
sents = []
cache = []
skipped = 0
skip = False
with open(filename, encoding=encoding) as infile:
for (i, line) in enumerate(infile):
line = line.rstrip()
if (len(line) == 0):
if (len(cache) > 0):
if (not skip):
sents.append(cache)
else:
skipped += 1
skip = False
cache = []
continue
array = line.split()
if (len(array) != 2):
skip = True
continue
(w, t) = array
cache.append([w, t])
if (len(cache) > 0):
if (not skip):
sents.append(cache)
else:
skipped += 1
cache = []
print('Skipped {} examples due to formatting issues.'.format(skipped))
return sents |
def download_weight(link, file_name, verbose=True):
response = requests.get(link, stream=True)
total_size_in_bytes = int(response.headers.get('content-length', 0))
block_size = 1024
progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True, desc='downloading defualt weights', disable=(False if verbose else True))
with open(file_name, 'wb') as file:
for data in response.iter_content(block_size):
progress_bar.update(len(data))
file.write(data)
progress_bar.close()
if ((total_size_in_bytes != 0) and (progress_bar.n != total_size_in_bytes)):
exit('ERROR, something went wrong (check your connection)') |
def save_pngs(chunk):
output_path = '/tmp/test/'
save_pngs_operator = SavePNGsOperator(output_path)
save_pngs_operator(chunk)
print('remove the temporary directory.')
shutil.rmtree(output_path) |
def get_the_pile_document_iterator(file_path: str) -> Iterator[str]:
with open(file_path, 'r') as f:
for line in f:
(yield json.loads(line)['text']) |
class CNNEvaluation(object):
def __init__(self, gpu_num, dataset='cifar10', verbose=True, epoch_num=50, batchsize=16, imgSize=32):
self.gpu_num = gpu_num
self.epoch_num = epoch_num
self.batchsize = batchsize
self.dataset = dataset
self.verbose = verbose
self.imgSize = imgSize
def __call__(self, net_lists):
evaluations = np.zeros(len(net_lists))
for i in np.arange(0, len(net_lists), self.gpu_num):
process_num = (np.min(((i + self.gpu_num), len(net_lists))) - i)
pool = NoDaemonProcessPool(process_num)
arg_data = [(cnn_eval, net_lists[(i + j)], j, self.epoch_num, self.batchsize, self.dataset, self.verbose, self.imgSize) for j in range(process_num)]
evaluations[i:(i + process_num)] = pool.map(arg_wrapper_mp, arg_data)
pool.terminate()
return evaluations |
class Cusps_class(Singleton, Parent):
def __init__(self):
Parent.__init__(self, self)
Element = Cusp
def _repr_(self):
return 'Set P^1(QQ) of all cusps'
def _latex_(self):
return '\\mathbf{P}^1(\\QQ)'
def __call__(self, x):
return Cusp(x)
def _coerce_map_from_(self, R):
if QQ.has_coerce_map_from(R):
return True
if (R is InfinityRing):
return True
return False
def _element_constructor_(self, x):
return Cusp(x) |
def register_Ns3PdcpTag_methods(root_module, cls):
cls.add_constructor([param('ns3::PdcpTag const &', 'arg0')])
cls.add_constructor([])
cls.add_constructor([param('ns3::Time', 'senderTimestamp')])
cls.add_method('Deserialize', 'void', [param('ns3::TagBuffer', 'i')], is_virtual=True)
cls.add_method('GetInstanceTypeId', 'ns3::TypeId', [], is_const=True, is_virtual=True)
cls.add_method('GetSenderTimestamp', 'ns3::Time', [], is_const=True)
cls.add_method('GetSerializedSize', 'uint32_t', [], is_const=True, is_virtual=True)
cls.add_method('GetTypeId', 'ns3::TypeId', [], is_static=True)
cls.add_method('Print', 'void', [param('std::ostream &', 'os')], is_const=True, is_virtual=True)
cls.add_method('Serialize', 'void', [param('ns3::TagBuffer', 'i')], is_const=True, is_virtual=True)
cls.add_method('SetSenderTimestamp', 'void', [param('ns3::Time', 'senderTimestamp')])
return |
class FeaturesManager():
_TASKS_TO_AUTOMODELS = {}
_TASKS_TO_TF_AUTOMODELS = {}
if is_torch_available():
_TASKS_TO_AUTOMODELS = {'default': AutoModel, 'masked-lm': AutoModelForMaskedLM, 'causal-lm': AutoModelForCausalLM, 'seq2seq-lm': AutoModelForSeq2SeqLM, 'sequence-classification': AutoModelForSequenceClassification, 'token-classification': AutoModelForTokenClassification, 'multiple-choice': AutoModelForMultipleChoice, 'object-detection': AutoModelForObjectDetection, 'question-answering': AutoModelForQuestionAnswering, 'image-classification': AutoModelForImageClassification, 'image-segmentation': AutoModelForImageSegmentation, 'masked-im': AutoModelForMaskedImageModeling, 'semantic-segmentation': AutoModelForSemanticSegmentation, 'vision2seq-lm': AutoModelForVision2Seq, 'speech2seq-lm': AutoModelForSpeechSeq2Seq}
if is_tf_available():
_TASKS_TO_TF_AUTOMODELS = {'default': TFAutoModel, 'masked-lm': TFAutoModelForMaskedLM, 'causal-lm': TFAutoModelForCausalLM, 'seq2seq-lm': TFAutoModelForSeq2SeqLM, 'sequence-classification': TFAutoModelForSequenceClassification, 'token-classification': TFAutoModelForTokenClassification, 'multiple-choice': TFAutoModelForMultipleChoice, 'question-answering': TFAutoModelForQuestionAnswering, 'semantic-segmentation': TFAutoModelForSemanticSegmentation}
_SUPPORTED_MODEL_TYPE = {'albert': supported_features_mapping('default', 'masked-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.albert.AlbertOnnxConfig'), 'bart': supported_features_mapping('default', 'default-with-past', 'causal-lm', 'causal-lm-with-past', 'seq2seq-lm', 'seq2seq-lm-with-past', 'sequence-classification', 'question-answering', onnx_config_cls='models.bart.BartOnnxConfig'), 'beit': supported_features_mapping('default', 'image-classification', onnx_config_cls='models.beit.BeitOnnxConfig'), 'bert': supported_features_mapping('default', 'masked-lm', 'causal-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.bert.BertOnnxConfig'), 'big-bird': supported_features_mapping('default', 'masked-lm', 'causal-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.big_bird.BigBirdOnnxConfig'), 'bigbird-pegasus': supported_features_mapping('default', 'default-with-past', 'causal-lm', 'causal-lm-with-past', 'seq2seq-lm', 'seq2seq-lm-with-past', 'sequence-classification', 'question-answering', onnx_config_cls='models.bigbird_pegasus.BigBirdPegasusOnnxConfig'), 'blenderbot': supported_features_mapping('default', 'default-with-past', 'causal-lm', 'causal-lm-with-past', 'seq2seq-lm', 'seq2seq-lm-with-past', onnx_config_cls='models.blenderbot.BlenderbotOnnxConfig'), 'blenderbot-small': supported_features_mapping('default', 'default-with-past', 'causal-lm', 'causal-lm-with-past', 'seq2seq-lm', 'seq2seq-lm-with-past', onnx_config_cls='models.blenderbot_small.BlenderbotSmallOnnxConfig'), 'bloom': supported_features_mapping('default', 'default-with-past', 'causal-lm', 'causal-lm-with-past', 'sequence-classification', 'token-classification', onnx_config_cls='models.bloom.BloomOnnxConfig'), 'camembert': supported_features_mapping('default', 'masked-lm', 'causal-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.camembert.CamembertOnnxConfig'), 'clip': supported_features_mapping('default', onnx_config_cls='models.clip.CLIPOnnxConfig'), 'codegen': supported_features_mapping('default', 'causal-lm', onnx_config_cls='models.codegen.CodeGenOnnxConfig'), 'convbert': supported_features_mapping('default', 'masked-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.convbert.ConvBertOnnxConfig'), 'convnext': supported_features_mapping('default', 'image-classification', onnx_config_cls='models.convnext.ConvNextOnnxConfig'), 'data2vec-text': supported_features_mapping('default', 'masked-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.data2vec.Data2VecTextOnnxConfig'), 'data2vec-vision': supported_features_mapping('default', 'image-classification', onnx_config_cls='models.data2vec.Data2VecVisionOnnxConfig'), 'deberta': supported_features_mapping('default', 'masked-lm', 'sequence-classification', 'token-classification', 'question-answering', onnx_config_cls='models.deberta.DebertaOnnxConfig'), 'deberta-v2': supported_features_mapping('default', 'masked-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.deberta_v2.DebertaV2OnnxConfig'), 'deit': supported_features_mapping('default', 'image-classification', onnx_config_cls='models.deit.DeiTOnnxConfig'), 'detr': supported_features_mapping('default', 'object-detection', 'image-segmentation', onnx_config_cls='models.detr.DetrOnnxConfig'), 'distilbert': supported_features_mapping('default', 'masked-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.distilbert.DistilBertOnnxConfig'), 'electra': supported_features_mapping('default', 'masked-lm', 'causal-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.electra.ElectraOnnxConfig'), 'flaubert': supported_features_mapping('default', 'masked-lm', 'causal-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.flaubert.FlaubertOnnxConfig'), 'gpt2': supported_features_mapping('default', 'default-with-past', 'causal-lm', 'causal-lm-with-past', 'sequence-classification', 'token-classification', onnx_config_cls='models.gpt2.GPT2OnnxConfig'), 'gptj': supported_features_mapping('default', 'default-with-past', 'causal-lm', 'causal-lm-with-past', 'question-answering', 'sequence-classification', onnx_config_cls='models.gptj.GPTJOnnxConfig'), 'gpt-neo': supported_features_mapping('default', 'default-with-past', 'causal-lm', 'causal-lm-with-past', 'sequence-classification', onnx_config_cls='models.gpt_neo.GPTNeoOnnxConfig'), 'groupvit': supported_features_mapping('default', onnx_config_cls='models.groupvit.GroupViTOnnxConfig'), 'ibert': supported_features_mapping('default', 'masked-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.ibert.IBertOnnxConfig'), 'imagegpt': supported_features_mapping('default', 'image-classification', onnx_config_cls='models.imagegpt.ImageGPTOnnxConfig'), 'layoutlm': supported_features_mapping('default', 'masked-lm', 'sequence-classification', 'token-classification', onnx_config_cls='models.layoutlm.LayoutLMOnnxConfig'), 'layoutlmv3': supported_features_mapping('default', 'question-answering', 'sequence-classification', 'token-classification', onnx_config_cls='models.layoutlmv3.LayoutLMv3OnnxConfig'), 'levit': supported_features_mapping('default', 'image-classification', onnx_config_cls='models.levit.LevitOnnxConfig'), 'longt5': supported_features_mapping('default', 'default-with-past', 'seq2seq-lm', 'seq2seq-lm-with-past', onnx_config_cls='models.longt5.LongT5OnnxConfig'), 'longformer': supported_features_mapping('default', 'masked-lm', 'multiple-choice', 'question-answering', 'sequence-classification', 'token-classification', onnx_config_cls='models.longformer.LongformerOnnxConfig'), 'marian': supported_features_mapping('default', 'default-with-past', 'seq2seq-lm', 'seq2seq-lm-with-past', 'causal-lm', 'causal-lm-with-past', onnx_config_cls='models.marian.MarianOnnxConfig'), 'mbart': supported_features_mapping('default', 'default-with-past', 'causal-lm', 'causal-lm-with-past', 'seq2seq-lm', 'seq2seq-lm-with-past', 'sequence-classification', 'question-answering', onnx_config_cls='models.mbart.MBartOnnxConfig'), 'mobilebert': supported_features_mapping('default', 'masked-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.mobilebert.MobileBertOnnxConfig'), 'mobilenet-v1': supported_features_mapping('default', 'image-classification', onnx_config_cls='models.mobilenet_v1.MobileNetV1OnnxConfig'), 'mobilenet-v2': supported_features_mapping('default', 'image-classification', onnx_config_cls='models.mobilenet_v2.MobileNetV2OnnxConfig'), 'mobilevit': supported_features_mapping('default', 'image-classification', onnx_config_cls='models.mobilevit.MobileViTOnnxConfig'), 'mt5': supported_features_mapping('default', 'default-with-past', 'seq2seq-lm', 'seq2seq-lm-with-past', onnx_config_cls='models.mt5.MT5OnnxConfig'), 'm2m-100': supported_features_mapping('default', 'default-with-past', 'seq2seq-lm', 'seq2seq-lm-with-past', onnx_config_cls='models.m2m_100.M2M100OnnxConfig'), 'owlvit': supported_features_mapping('default', onnx_config_cls='models.owlvit.OwlViTOnnxConfig'), 'perceiver': supported_features_mapping('image-classification', 'masked-lm', 'sequence-classification', onnx_config_cls='models.perceiver.PerceiverOnnxConfig'), 'poolformer': supported_features_mapping('default', 'image-classification', onnx_config_cls='models.poolformer.PoolFormerOnnxConfig'), 'rembert': supported_features_mapping('default', 'masked-lm', 'causal-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.rembert.RemBertOnnxConfig'), 'resnet': supported_features_mapping('default', 'image-classification', onnx_config_cls='models.resnet.ResNetOnnxConfig'), 'roberta': supported_features_mapping('default', 'masked-lm', 'causal-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.roberta.RobertaOnnxConfig'), 'roformer': supported_features_mapping('default', 'masked-lm', 'causal-lm', 'sequence-classification', 'token-classification', 'multiple-choice', 'question-answering', 'token-classification', onnx_config_cls='models.roformer.RoFormerOnnxConfig'), 'segformer': supported_features_mapping('default', 'image-classification', 'semantic-segmentation', onnx_config_cls='models.segformer.SegformerOnnxConfig'), 'squeezebert': supported_features_mapping('default', 'masked-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.squeezebert.SqueezeBertOnnxConfig'), 'swin': supported_features_mapping('default', 'image-classification', onnx_config_cls='models.swin.SwinOnnxConfig'), 't5': supported_features_mapping('default', 'default-with-past', 'seq2seq-lm', 'seq2seq-lm-with-past', onnx_config_cls='models.t5.T5OnnxConfig'), 'vision-encoder-decoder': supported_features_mapping('vision2seq-lm', onnx_config_cls='models.vision_encoder_decoder.VisionEncoderDecoderOnnxConfig'), 'vit': supported_features_mapping('default', 'image-classification', onnx_config_cls='models.vit.ViTOnnxConfig'), 'whisper': supported_features_mapping('default', 'default-with-past', 'speech2seq-lm', 'speech2seq-lm-with-past', onnx_config_cls='models.whisper.WhisperOnnxConfig'), 'xlm': supported_features_mapping('default', 'masked-lm', 'causal-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.xlm.XLMOnnxConfig'), 'xlm-roberta': supported_features_mapping('default', 'masked-lm', 'causal-lm', 'sequence-classification', 'multiple-choice', 'token-classification', 'question-answering', onnx_config_cls='models.xlm_roberta.XLMRobertaOnnxConfig'), 'yolos': supported_features_mapping('default', 'object-detection', onnx_config_cls='models.yolos.YolosOnnxConfig')}
AVAILABLE_FEATURES = sorted(reduce((lambda s1, s2: (s1 | s2)), (v.keys() for v in _SUPPORTED_MODEL_TYPE.values())))
def get_supported_features_for_model_type(model_type: str, model_name: Optional[str]=None) -> Dict[(str, Callable[([PretrainedConfig], OnnxConfig)])]:
model_type = model_type.lower()
if (model_type not in FeaturesManager._SUPPORTED_MODEL_TYPE):
model_type_and_model_name = (f'{model_type} ({model_name})' if model_name else model_type)
raise KeyError(f'{model_type_and_model_name} is not supported yet. Only {list(FeaturesManager._SUPPORTED_MODEL_TYPE.keys())} are supported. If you want to support {model_type} please propose a PR or open up an issue.')
return FeaturesManager._SUPPORTED_MODEL_TYPE[model_type]
def feature_to_task(feature: str) -> str:
return feature.replace('-with-past', '')
def _validate_framework_choice(framework: str):
if (framework not in ['pt', 'tf']):
raise ValueError(f'Only two frameworks are supported for ONNX export: pt or tf, but {framework} was provided.')
elif ((framework == 'pt') and (not is_torch_available())):
raise RuntimeError('Cannot export model to ONNX using PyTorch because no PyTorch package was found.')
elif ((framework == 'tf') and (not is_tf_available())):
raise RuntimeError('Cannot export model to ONNX using TensorFlow because no TensorFlow package was found.')
def get_model_class_for_feature(feature: str, framework: str='pt') -> Type:
task = FeaturesManager.feature_to_task(feature)
FeaturesManager._validate_framework_choice(framework)
if (framework == 'pt'):
task_to_automodel = FeaturesManager._TASKS_TO_AUTOMODELS
else:
task_to_automodel = FeaturesManager._TASKS_TO_TF_AUTOMODELS
if (task not in task_to_automodel):
raise KeyError(f'Unknown task: {feature}. Possible values are {list(FeaturesManager._TASKS_TO_AUTOMODELS.values())}')
return task_to_automodel[task]
def determine_framework(model: str, framework: str=None) -> str:
if (framework is not None):
return framework
framework_map = {'pt': 'PyTorch', 'tf': 'TensorFlow'}
exporter_map = {'pt': 'torch', 'tf': 'tf2onnx'}
if os.path.isdir(model):
if os.path.isfile(os.path.join(model, WEIGHTS_NAME)):
framework = 'pt'
elif os.path.isfile(os.path.join(model, TF2_WEIGHTS_NAME)):
framework = 'tf'
else:
raise FileNotFoundError(f'Cannot determine framework from given checkpoint location. There should be a {WEIGHTS_NAME} for PyTorch or {TF2_WEIGHTS_NAME} for TensorFlow.')
logger.info(f'Local {framework_map[framework]} model found.')
elif is_torch_available():
framework = 'pt'
elif is_tf_available():
framework = 'tf'
else:
raise EnvironmentError('Neither PyTorch nor TensorFlow found in environment. Cannot export to ONNX.')
logger.info(f'Framework not requested. Using {exporter_map[framework]} to export to ONNX.')
return framework
def get_model_from_feature(feature: str, model: str, framework: str=None, cache_dir: str=None) -> Union[('PreTrainedModel', 'TFPreTrainedModel')]:
framework = FeaturesManager.determine_framework(model, framework)
model_class = FeaturesManager.get_model_class_for_feature(feature, framework)
try:
model = model_class.from_pretrained(model, cache_dir=cache_dir)
except OSError:
if (framework == 'pt'):
logger.info('Loading TensorFlow model in PyTorch before exporting to ONNX.')
model = model_class.from_pretrained(model, from_tf=True, cache_dir=cache_dir)
else:
logger.info('Loading PyTorch model in TensorFlow before exporting to ONNX.')
model = model_class.from_pretrained(model, from_pt=True, cache_dir=cache_dir)
return model
def check_supported_model_or_raise(model: Union[('PreTrainedModel', 'TFPreTrainedModel')], feature: str='default') -> Tuple[(str, Callable)]:
model_type = model.config.model_type.replace('_', '-')
model_name = getattr(model, 'name', '')
model_features = FeaturesManager.get_supported_features_for_model_type(model_type, model_name=model_name)
if (feature not in model_features):
raise ValueError(f"{model.config.model_type} doesn't support feature {feature}. Supported values are: {model_features}")
return (model.config.model_type, FeaturesManager._SUPPORTED_MODEL_TYPE[model_type][feature])
def get_config(model_type: str, feature: str) -> OnnxConfig:
return FeaturesManager._SUPPORTED_MODEL_TYPE[model_type][feature] |
def test_test_dataloader():
movieLensDataHandler = AEDataHandler('MovieLensSmall', train_data_path, validation_input_data_path, validation_output_data_path, test_input_data_path, test_output_data_path)
test_dataloader = movieLensDataHandler.get_test_dataloader()
count = 0
for batch in test_dataloader:
assert (500 == len(batch[0]))
assert (500 == len(batch[1]))
assert (8936 == len(batch[0][0]))
assert (8936 == len(batch[1][0]))
count += 1
assert (4 == count)
count = 0
for batch in test_dataloader:
assert (500 == len(batch[0]))
assert (500 == len(batch[1]))
count += 1
assert (4 == count) |
def distance_transform_cdt(input, metric='chessboard', return_distances=True, return_indices=False, distances=None, indices=None):
if ((not return_distances) and (not return_indices)):
msg = 'at least one of distances/indices must be specified'
raise RuntimeError(msg)
ft_inplace = isinstance(indices, numpy.ndarray)
dt_inplace = isinstance(distances, numpy.ndarray)
input = numpy.asarray(input)
if (metric in ['taxicab', 'cityblock', 'manhattan']):
rank = input.ndim
metric = generate_binary_structure(rank, 1)
elif (metric == 'chessboard'):
rank = input.ndim
metric = generate_binary_structure(rank, rank)
else:
try:
metric = numpy.asarray(metric)
except Exception:
raise RuntimeError('invalid metric provided')
for s in metric.shape:
if (s != 3):
raise RuntimeError('metric sizes must be equal to 3')
if (not metric.flags.contiguous):
metric = metric.copy()
if dt_inplace:
if (distances.dtype.type != numpy.int32):
raise RuntimeError('distances must be of int32 type')
if (distances.shape != input.shape):
raise RuntimeError('distances has wrong shape')
dt = distances
dt[...] = numpy.where(input, (- 1), 0).astype(numpy.int32)
else:
dt = numpy.where(input, (- 1), 0).astype(numpy.int32)
rank = dt.ndim
if return_indices:
sz = numpy.prod(dt.shape, axis=0)
ft = numpy.arange(sz, dtype=numpy.int32)
ft.shape = dt.shape
else:
ft = None
_nd_image.distance_transform_op(metric, dt, ft)
dt = dt[tuple(([slice(None, None, (- 1))] * rank))]
if return_indices:
ft = ft[tuple(([slice(None, None, (- 1))] * rank))]
_nd_image.distance_transform_op(metric, dt, ft)
dt = dt[tuple(([slice(None, None, (- 1))] * rank))]
if return_indices:
ft = ft[tuple(([slice(None, None, (- 1))] * rank))]
ft = numpy.ravel(ft)
if ft_inplace:
if (indices.dtype.type != numpy.int32):
raise RuntimeError('indices must of int32 type')
if (indices.shape != ((dt.ndim,) + dt.shape)):
raise RuntimeError('indices has wrong shape')
tmp = indices
else:
tmp = numpy.indices(dt.shape, dtype=numpy.int32)
for ii in range(tmp.shape[0]):
rtmp = numpy.ravel(tmp[(ii, ...)])[ft]
rtmp.shape = dt.shape
tmp[(ii, ...)] = rtmp
ft = tmp
result = []
if (return_distances and (not dt_inplace)):
result.append(dt)
if (return_indices and (not ft_inplace)):
result.append(ft)
if (len(result) == 2):
return tuple(result)
elif (len(result) == 1):
return result[0]
else:
return None |
class AMAZON2Processor(TextClassProcessor):
def __init__(self):
self.has_title = True
def get_labels(self):
return [str(i) for i in range(1, 3)]
def get_train_size(self):
return 3600000
def get_dev_size(self):
return 400000
def get_unsup_examples(self, raw_data_dir, unsup_set):
if (unsup_set == 'unsup_in'):
return self._create_examples(self._read_tsv(os.path.join(raw_data_dir, 'train.csv'), quotechar='"', delimiter=','), 'unsup_in', skip_unsup=False)
else:
dir_cell = raw_data_dir[5:7]
unsup_dir = None
return self._create_examples(self._read_tsv(os.path.join(unsup_dir, '{:s}.csv'.format(unsup_set)), quotechar='"', delimiter=','), unsup_set, skip_unsup=False) |
class DefaultJsonEncoder(json.JSONEncoder):
def default(self, o):
if isinstance(o, np.ndarray):
return o.tolist()
if isinstance(o, np.generic):
return o.item()
if (isinstance(o, pd.DataFrame) or isinstance(o, pd.Series)):
return o.to_dict()
if isinstance(o, PilImage.Image):
return np.array(o).tolist()
if isinstance(o, ExplanationBase):
return {'module': o.__class__.__module__, 'class': o.__class__.__name__, 'data': {k: deepcopy(v) for (k, v) in o.__dict__.items()}}
return super().default(o) |
def main():
config = parser.parse_args()
fine_LSTM = MyModel.fine_LSTM(config).cuda(config.use_gpu)
coarseNet = MyModel.coarseNet(config).cuda(config.use_gpu)
if (config.stage == 'test'):
fine_LSTM = torch.load(((('output/' + '730') + config.testName) + 'fine_LSTM.pkl'), map_location=(lambda storage, loc: storage.cuda(config.use_gpu)))
coarseNet = torch.load(((('output/' + '730') + config.testName) + 'coarse.pkl'), map_location=(lambda storage, loc: storage.cuda(config.use_gpu)))
dataRoot = 'processed_data/'
transform_origin = transforms.Compose([Rescale(config.origin_image_size), ToTensor()])
train_dataset_origin = LandmarksDataset(csv_file=(dataRoot + config.traincsv), root_dir=(dataRoot + 'images'), transform=transform_origin, landmarksNum=config.landmarkNum)
val_dataset = LandmarksDataset(csv_file=(dataRoot + config.testcsv), root_dir=(dataRoot + 'images'), transform=transform_origin, landmarksNum=config.landmarkNum)
train_dataloader = []
val_dataloader = []
train_dataloader_t = DataLoader(train_dataset_origin, batch_size=config.batchSize, shuffle=False, num_workers=0)
if (config.stage == 'train'):
for data in train_dataloader_t:
train_dataloader.append(data)
val_dataloader_t = DataLoader(val_dataset, batch_size=config.batchSize, shuffle=False, num_workers=0)
for data in val_dataloader_t:
val_dataloader.append(data)
print(len(train_dataloader), len(val_dataloader))
dataloaders = {'train': train_dataloader, 'val': val_dataloader}
criterion_coarse = LossFunction.coarse_heatmap(config)
criterion_fine = LossFunction.fine_heatmap(config)
params = (list(coarseNet.parameters()) + list(fine_LSTM.parameters()))
optimizer_ft = optim.Adam(params)
TrainNet.train_model(coarseNet, fine_LSTM, dataloaders, criterion_coarse, criterion_fine, optimizer_ft, config) |
class CrossEntropyLoss2d(nn.Module):
def __init__(self, weight=None, ignore_index=255, reduction='mean', label_smoothing=0.0, loss_weight=1.0, loss_name='ce_loss'):
super(CrossEntropyLoss2d, self).__init__()
self.loss_weight = loss_weight
self._loss_name = loss_name
self.criterion = nn.CrossEntropyLoss(weight=weight, ignore_index=ignore_index, reduction=reduction, label_smoothing=label_smoothing)
def forward(self, pred, target):
return (self.loss_weight * self.criterion(pred, target.long()))
def loss_name(self):
return self._loss_name |
(**njit_dict_no_parallel)
def deposition_estimator_kasen(energy, ejecta_density, iron_group_fraction):
return ((get_average_compton_fraction(energy) * compton_opacity_calculation(energy, ejecta_density)) + photoabsorption_opacity_calculation(energy, ejecta_density, iron_group_fraction)) |
class LabelSmoothingCrossEntropy(nn.Module):
def __init__(self, : float=0.1, reduction='mean'):
super().__init__()
(self., self.reduction) = (, reduction)
def forward(self, output, target):
c = output.size()[(- 1)]
log_preds = F.log_softmax(output, dim=(- 1))
loss = reduce_loss((- log_preds.sum(dim=(- 1))), self.reduction)
nll = F.nll_loss(log_preds, target, reduction=self.reduction)
return (((1 - self.) * nll) + (self. * (loss / c))) |
def make_tree(cfg, logger=None):
if (logger is not None):
logger('\n[Preparing loss...]')
loss_file = cfg.loss
if (not loss_file.lower().endswith('.txt')):
loss_file += '.txt'
with open(loss_file, 'r') as f:
lines = f.read().splitlines()
lines = parse(lines)
hparams = parse(cfg.hparams)
for (k, v) in hparams.items():
if (k in lines):
lines[k] = v
for (k, v) in lines.items():
meta_k = '$({})'.format(k)
for (kk, vv) in lines.items():
if (meta_k in vv):
lines[kk] = vv.replace(meta_k, v)
root = node.LossNode('total', cfg, lookup=lines)
if (logger is not None):
logger(root)
with open(logger.get_path('loss.txt'), 'w') as f:
for (k, v) in lines.items():
f.write('{}={}\n'.format(k, v))
root = gpu_utils.obj2device(root)
return root |
def _fused_bias_act_cuda(x, b, axis, act, alpha, gain):
x = tf.convert_to_tensor(x)
empty_tensor = tf.constant([], dtype=x.dtype)
b = (tf.convert_to_tensor(b) if (b is not None) else empty_tensor)
act_spec = activation_funcs[act]
assert ((len(b.shape) == 1) and ((b.shape[0] == 0) or (b.shape[0] == x.shape[axis])))
assert ((b.shape[0] == 0) or (0 <= axis < len(x.shape)))
if (alpha is None):
alpha = act_spec.def_alpha
if (gain is None):
gain = act_spec.def_gain
if ((act == 'linear') and (b is None) and (gain == 1.0)):
return x
if (act_spec.cuda_idx is None):
return _fused_bias_act_ref(x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain)
cuda_kernel = _get_plugin().fused_bias_act
cuda_kwargs = dict(axis=axis, act=act_spec.cuda_idx, alpha=alpha, gain=gain)
def func_y(x, b):
y = cuda_kernel(x=x, b=b, ref=empty_tensor, grad=0, **cuda_kwargs)
y.set_shape(x.shape)
return y
def grad_dx(dy, x, y):
ref = {'x': x, 'y': y}[act_spec.ref]
dx = cuda_kernel(x=dy, b=empty_tensor, ref=ref, grad=1, **cuda_kwargs)
dx.set_shape(x.shape)
return dx
def grad_db(dx):
if (b.shape[0] == 0):
return empty_tensor
db = dx
if (axis < (len(x.shape) - 1)):
db = tf.reduce_sum(db, list(range((axis + 1), len(x.shape))))
if (axis > 0):
db = tf.reduce_sum(db, list(range(axis)))
db.set_shape(b.shape)
return db
def grad2_d_dy(d_dx, d_db, x, y):
ref = {'x': x, 'y': y}[act_spec.ref]
d_dy = cuda_kernel(x=d_dx, b=d_db, ref=ref, grad=1, **cuda_kwargs)
d_dy.set_shape(x.shape)
return d_dy
def grad2_d_x(d_dx, d_db, x, y):
ref = {'x': x, 'y': y}[act_spec.ref]
d_x = cuda_kernel(x=d_dx, b=d_db, ref=ref, grad=2, **cuda_kwargs)
d_x.set_shape(x.shape)
return d_x
_gradient
def func_zero_2nd_grad(x, b):
y = func_y(x, b)
_gradient
def grad(dy):
dx = grad_dx(dy, x, y)
db = grad_db(dx)
def grad2(d_dx, d_db):
d_dy = grad2_d_dy(d_dx, d_db, x, y)
return d_dy
return ((dx, db), grad2)
return (y, grad)
_gradient
def func_nonzero_2nd_grad(x, b):
y = func_y(x, b)
def grad_wrap(dy):
_gradient
def grad_impl(dy, x):
dx = grad_dx(dy, x, y)
db = grad_db(dx)
def grad2(d_dx, d_db):
d_dy = grad2_d_dy(d_dx, d_db, x, y)
d_x = grad2_d_x(d_dx, d_db, x, y)
return (d_dy, d_x)
return ((dx, db), grad2)
return grad_impl(dy, x)
return (y, grad_wrap)
if act_spec.zero_2nd_grad:
return func_zero_2nd_grad(x, b)
return func_nonzero_2nd_grad(x, b) |
def zou_et_al_criterion_rescaling(criterion, n_samples, noise_variance):
return ((criterion - (n_samples * np.log(((2 * np.pi) * noise_variance)))) - n_samples) |
def rerank(model_file, ctx_file, rnk_file, score=False):
output_wfile = open(((rnk_file + '_LEN') + ('.f' if score else '.gen')), 'w')
begin = True
for (ctx_line, rnk_line) in itertools.izip(open(ctx_file), open(rnk_file)):
suffix = ctx_line.strip().split('\t')
candidates = rnk_line.strip().split('\t')
cscores = map(len, candidates)
wscores = map(len, [x.split() for x in candidates])
if (not score):
raise Exception('Not supported!')
else:
if begin:
((print >> output_wfile), 'cLEN wLEN allLEN')
begin = False
for (wscore, cscore) in zip(wscores, cscores):
((print >> output_wfile), cscore, wscore, len(suffix))
output_wfile.close() |
class DecoderBlockPreNorm(DecoderBlock):
def __init__(self, *kargs, **kwargs):
super(DecoderBlockPreNorm, self).__init__(*kargs, **kwargs)
def forward(self, inputs, context, state=None):
x = inputs
res = x
x = (self.lnorm1(x) if hasattr(self, 'lnorm1') else x)
if self.stateful:
(x, state) = self.state_block(x, state)
else:
if (state is None):
x_past = x
else:
x_past = torch.cat((state, x), 1)
(x, _) = self.masked_attention(x, x_past, x_past)
state = x_past
if hasattr(self, 'state_proj'):
x = self.state_proj(x)
x = self.dropout(x).add(res)
res = x
x = (self.lnorm2(x) if hasattr(self, 'lnorm2') else x)
(x, attn_enc) = self.attention(x, context, context)
x = self.dropout(x).add_(res)
res = x
x = (self.lnorm3(x) if hasattr(self, 'lnorm3') else x)
x = self.fc(x)
x = self.dropout(x).add_(res)
return (x, attn_enc, state) |
def mask_tokens(inputs, mlm_probability, tokenizer, special_tokens_mask):
labels = np.copy(inputs)
probability_matrix = np.random.random_sample(labels.shape)
special_tokens_mask = special_tokens_mask.astype(np.bool_)
probability_matrix[special_tokens_mask] = 0.0
masked_indices = (probability_matrix > (1 - mlm_probability))
labels[(~ masked_indices)] = (- 100)
indices_replaced = ((np.random.random_sample(labels.shape) < 0.8) & masked_indices)
inputs[indices_replaced] = tokenizer.convert_tokens_to_ids(tokenizer.mask_token)
indices_random = (((np.random.random_sample(labels.shape) < 0.5) & masked_indices) & (~ indices_replaced))
random_words = np.random.randint(low=0, high=len(tokenizer), size=np.count_nonzero(indices_random), dtype=np.int64)
inputs[indices_random] = random_words
return (inputs, labels) |
def load_dataset(args):
transform_px = tr.Compose([tr.ToTensor(), (lambda x: (x * 255))])
if (args.dataset == 'cifar100'):
cls = dataset_without_label(torchvision.datasets.CIFAR100)
test_dataset = cls(root=args.data_path, transform=transform_px)
elif (args.dataset in ['celeba', 'img32', 'tinyimg']):
cls = dataset_without_label(torchvision.datasets.ImageFolder)
set_name = ('train' if (args.dataset in ['celeba']) else 'val')
test_dataset = cls(root=os.path.join(args.data_path, set_name), transform=transform_px)
else:
assert False, ('dataset %s' % args.dataset)
return test_dataset |
class FeatureSparseToDense(ModelLayer):
def __init__(self, model, input_record, input_specs, name='feature_sparse_to_dense', default_dense_value=None, **kwargs):
super(FeatureSparseToDense, self).__init__(model, name, input_record, **kwargs)
if (default_dense_value is None):
default_dense_value = 0.0
default_dense_value = float(default_dense_value)
assert (np.isnan(default_dense_value) or (default_dense_value == 0.0)), 'default_dense_value can only be 0.0 or NaN'
self.input_specs = input_specs
self.default_float_value = (model.global_constants['NAN'] if np.isnan(default_dense_value) else model.global_constants['ZERO'])
self.zero_range = model.global_constants['ZERO_RANGE']
outputs = []
for (field, feature_specs) in self.input_specs:
assert (len(feature_specs.feature_names) == len(feature_specs.feature_ids))
if (feature_specs.feature_type == 'FLOAT'):
outputs.append((field, schema.Scalar((np.float32, (len(feature_specs.feature_ids),)), self.get_next_blob_reference((field + '_output')))))
elif (feature_specs.feature_type == 'ID_LIST'):
outputs.append((field, schema.Struct(('ranges', schema.Scalar((np.int32, (len(feature_specs.feature_ids), 2)), self.get_next_blob_reference((field + '_ranges')))), ('values', schema.Scalar(np.int64, self.get_next_blob_reference((field + '_values')))))))
elif (feature_specs.feature_type == 'ID_SCORE_LIST'):
outputs.append((field, schema.Struct(('ranges', schema.Scalar((np.int32, (len(feature_specs.feature_ids), 2)), self.get_next_blob_reference((field + '_ranges')))), ('ids', schema.Scalar(np.int64, self.get_next_blob_reference((field + '_ids')))), ('scores', schema.Scalar(np.float32, self.get_next_blob_reference((field + '_scores')))))))
elif (feature_specs.feature_type == 'EMBEDDING'):
outputs.append((field, schema.Struct(('ranges', schema.Scalar((np.int32, (len(feature_specs.feature_ids), 2)), self.get_next_blob_reference((field + '_ranges')))), ('values', schema.Scalar(np.float32, self.get_next_blob_reference((field + '_values')))))))
elif (feature_specs.feature_type == 'GENERIC_FEATURE'):
outputs.append((field, schema.Struct(('ranges', schema.Scalar((np.int32, (len(feature_specs.feature_ids), 2)), self.get_next_blob_reference((field + '_ranges')))), ('values', schema.Scalar(np.float32, self.get_next_blob_reference((field + '_values')))))))
else:
raise TypeError('Unsupported input type: {0}'.format(feature_specs.feature_type))
self.output_schema = schema.Struct(*outputs)
for (field, feature_specs) in input_specs:
schema.attach_metadata_to_scalars(self.output_schema[field], schema.Metadata(feature_specs=feature_specs))
def add_ops(self, net):
record = self.input_record
for (field, feature_specs) in self.input_specs:
if (feature_specs.feature_type == 'FLOAT'):
net.SparseToDenseMask([record[field].keys(), record[field].values(), self.default_float_value, record[field].lengths()], [self.output_schema[field]()], mask=feature_specs.feature_ids)
elif (feature_specs.feature_type == 'ID_LIST'):
id_list_ranges = net.LengthsToRanges(record[field].values.lengths(), net.NextScopedBlob('id_list_ranges'))
net.SparseToDenseMask([record[field].keys(), id_list_ranges, self.zero_range, record[field].lengths()], self.output_schema[field].ranges(), mask=feature_specs.feature_ids)
net.Alias(record[field].values.items(), self.output_schema[field].values())
elif (feature_specs.feature_type == 'ID_SCORE_LIST'):
id_list_ranges = net.LengthsToRanges(record[field].values.lengths(), net.NextScopedBlob('id_score_list_ranges'))
net.SparseToDenseMask([record[field].keys(), id_list_ranges, self.zero_range, record[field].lengths()], self.output_schema[field].ranges(), mask=feature_specs.feature_ids)
net.Alias(record[field].values.keys(), self.output_schema[field].ids())
net.Alias(record[field].values.values(), self.output_schema[field].scores())
elif (feature_specs.feature_type == 'EMBEDDING'):
ranges = net.LengthsToRanges(record[field].values.lengths(), net.NextScopedBlob('embeddings_ranges'))
net.SparseToDenseMask([record[field].keys(), ranges, self.zero_range, record[field].lengths()], self.output_schema[field].ranges(), mask=feature_specs.feature_ids)
net.Alias(record[field].values.items(), self.output_schema[field].values())
elif (feature_specs.feature_type == 'GENERIC_FEATURE'):
(feature_lengths_blob, feature_ids_blob, value_lengths_blob, value_values_blob) = net.ParseGeneric([record[field]()], ['feature_lengths', 'feature_ids', 'value_lengths', 'value_values'], feature_type_enum=1)
ranges = net.LengthsToRanges(value_lengths_blob, net.NextScopedBlob('generics_ranges'))
net.SparseToDenseMask([feature_ids_blob, ranges, self.zero_range, feature_lengths_blob], self.output_schema[field].ranges(), mask=feature_specs.feature_ids)
net.Alias(value_values_blob, self.output_schema[field].values())
def get_metadata(self):
metadata = []
for (field, feature_specs) in self.input_specs:
metadata.append(({'type': feature_specs.feature_type, 'names': feature_specs.feature_names, 'ids': feature_specs.feature_ids}, self.output_schema[field].field_blobs(), self.output_schema[field].field_types()))
if (feature_specs.feature_type == 'FLOAT'):
metadata[(- 1)][0]['cardinality'] = 1
return metadata
def get_accessed_features(self):
accessed_features = defaultdict(list)
for (field, feature_specs) in self.input_specs:
accessed_features[field].append(AccessedFeatures(feature_specs.feature_type, set(feature_specs.feature_ids)))
return accessed_features |
class SegmentationSoftmax(Layer):
output_layer = True
def __init__(self, name, inputs, dataset, network_input_dict, tower_setup, resize_targets=False, resize_logits=False, loss='ce', fraction=None):
super().__init__()
self.n_classes = dataset.num_classes()
targets = network_input_dict[DataKeys.SEGMENTATION_LABELS]
assert (targets.get_shape().ndims == 4), targets.get_shape()
assert (not (resize_targets and resize_logits))
assert (len(inputs) == 1), len(inputs)
logits = inputs[0]
assert (logits.get_shape()[(- 1)] == self.n_classes)
if resize_targets:
print('warning, using resize_targets=True, so the resulting scores will not be computed at the initial resolution', file=log.v1)
targets = tf.image.resize_nearest_neighbor(targets, tf.shape(logits)[1:3])
if resize_logits:
logits = tf.image.resize_images(logits, tf.shape(targets)[1:3])
output = tf.nn.softmax(logits, (- 1), 'softmax')
self.outputs = [output]
if (self.n_classes == 2):
self.extractions[Extractions.SEGMENTATION_POSTERIORS] = output[(..., 1)]
class_pred = tf.argmax(logits, axis=3)
targets = tf.cast(targets, tf.int64)
targets = tf.squeeze(targets, axis=3)
self.loss = self._create_loss(loss, fraction, logits, targets)
self.losses.append(self.loss)
batch_size = smart_shape(targets)[0]
if ((not tower_setup.is_training) and (batch_size == 1) and (DataKeys.SEGMENTATION_LABELS_ORIGINAL_SIZE in network_input_dict)):
print(tower_setup.network_name, name, ': Using SEGMENTATION_LABELS_ORIGINAL_SIZE for calculating IoU', file=log.v1)
targets_for_measures = network_input_dict[DataKeys.SEGMENTATION_LABELS_ORIGINAL_SIZE]
targets_for_measures = tf.cast(targets_for_measures, tf.int64)
targets_for_measures = tf.squeeze(targets_for_measures, axis=3)
self.extractions[Extractions.SEGMENTATION_MASK_INPUT_SIZE] = class_pred
class_pred_for_measures = self._resize_predictions_to_original_size(class_pred, network_input_dict, targets_for_measures)
self.extractions[Extractions.SEGMENTATION_MASK_ORIGINAL_SIZE] = class_pred_for_measures
else:
print(tower_setup.network_name, name, ': Using SEGMENTATION_LABELS for calculating IoU', file=log.v1)
targets_for_measures = targets
class_pred_for_measures = class_pred
self.extractions[Extractions.SEGMENTATION_MASK_INPUT_SIZE] = class_pred_for_measures
self.measures = self._create_measures(class_pred_for_measures, targets_for_measures)
self.add_image_summary(tf.cast(tf.expand_dims(class_pred, axis=3), tf.float32), 'predicted labels')
self.add_scalar_summary(self.loss, 'loss')
def _create_loss(self, loss_str, fraction, logits, targets):
raw_ce = None
n_valid_pixels_per_im = None
if ('ce' in loss_str):
no_void_label_mask = tf.not_equal(targets, VOID_LABEL)
targets_no_void = tf.where(no_void_label_mask, targets, tf.zeros_like(targets))
raw_ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=targets_no_void, name='ce')
raw_ce *= tf.cast(no_void_label_mask, tf.float32)
n_valid_pixels_per_im = tf.reduce_sum(tf.cast(no_void_label_mask, tf.int32), axis=[1, 2])
if (loss_str == 'ce'):
ce_per_im = tf.reduce_sum(raw_ce, axis=[1, 2])
ce_per_im /= tf.cast(tf.maximum(n_valid_pixels_per_im, 1), tf.float32)
ce_total = tf.reduce_mean(ce_per_im, axis=0)
loss = ce_total
elif (loss_str == 'bootstrapped_ce'):
loss = bootstrapped_ce_loss(raw_ce, fraction, n_valid_pixels_per_im)
elif (loss_str == 'class_balanced_ce'):
loss = class_balanced_ce_loss(raw_ce, targets, self.n_classes)
else:
assert False, ('unknown loss', loss_str)
return loss
def _create_measures(self, pred, targets):
n_examples = tf.shape(targets)[0]
measures = {Measures.LOSS: (self.loss * tf.cast(n_examples, tf.float32)), Measures.N_EXAMPLES: n_examples}
if (self.n_classes == 2):
binary_measures = compute_measures_for_binary_segmentation_tf(pred, targets)
measures.update(binary_measures)
return measures
def _resize_predictions_to_original_size(class_pred, network_input_dict, targets_for_measures):
if (DataKeys.CROP_BOXES_y0x0y1x1 in network_input_dict):
crop_box = tf.squeeze(network_input_dict[DataKeys.CROP_BOXES_y0x0y1x1], axis=0)
(y0, x0, y1, x1) = tf.unstack(crop_box)
height_before_resize = (y1 - y0)
width_before_resize = (x1 - x0)
else:
(height_before_resize, width_before_resize) = tf.shape(targets_for_measures)[1:3]
(y0, x0, y1, x1) = (None, None, None, None)
class_pred_original_size = tf.squeeze(tf.image.resize_nearest_neighbor(class_pred[(..., tf.newaxis)], [height_before_resize, width_before_resize]), axis=(- 1))
if (DataKeys.CROP_BOXES_y0x0y1x1 in network_input_dict):
pad_y_l = y0
pad_y_r = (tf.shape(targets_for_measures)[1] - y1)
pad_x_l = x0
pad_x_r = (tf.shape(targets_for_measures)[2] - x1)
class_pred_for_measures = tf.pad(class_pred_original_size, [[0, 0], [pad_y_l, pad_y_r], [pad_x_l, pad_x_r]])
else:
class_pred_for_measures = class_pred_original_size
return class_pred_for_measures |
class anglit_gen(rv_continuous):
def _shape_info(self):
return []
def _pdf(self, x):
return np.cos((2 * x))
def _cdf(self, x):
return (np.sin((x + (np.pi / 4))) ** 2.0)
def _sf(self, x):
return (np.cos((x + (np.pi / 4))) ** 2.0)
def _ppf(self, q):
return (np.arcsin(np.sqrt(q)) - (np.pi / 4))
def _stats(self):
return (0.0, (((np.pi * np.pi) / 16) - 0.5), 0.0, (((- 2) * ((np.pi ** 4) - 96)) / (((np.pi * np.pi) - 8) ** 2)))
def _entropy(self):
return (1 - np.log(2)) |
def add_context(stat: Stat, context: MetricContext) -> Stat:
return Stat(replace(stat.name, split=context.split, sub_split=context.sub_split, perturbation=context.perturbation)).merge(stat) |
def get_keras_lstm(num_buckets, embed_dim=16, rnn_state_size=64):
lstm_model = tf.keras.Sequential()
lstm_model.add(tf.keras.layers.Embedding(num_buckets, embed_dim))
lstm_model.add(tf.keras.layers.LSTM(rnn_state_size, activation=tf.nn.relu))
lstm_model.add(tf.keras.layers.Dense(1, activation=tf.nn.sigmoid))
lstm_model.compile('Adagrad', 'binary_crossentropy', metrics=['accuracy'])
return lstm_model |
_utils.test(arch=[ti.cuda, ti.vulkan, ti.amdgpu])
def test_shared_array_atomics():
N = 256
block_dim = 32
def atomic_test(out: ti.types.ndarray()):
ti.loop_config(block_dim=block_dim)
for i in range(N):
tid = (i % block_dim)
val = tid
sharr = ti.simt.block.SharedArray((block_dim,), ti.i32)
sharr[tid] = val
ti.simt.block.sync()
sharr[0] += val
ti.simt.block.sync()
out[i] = sharr[tid]
arr = ti.ndarray(ti.i32, N)
atomic_test(arr)
ti.sync()
sum = ((block_dim * (block_dim - 1)) // 2)
assert (arr[0] == sum)
assert (arr[32] == sum)
assert (arr[128] == sum)
assert (arr[224] == sum) |
def realize_text_and_extract_scene(scene, template, filter_objs):
default_list = (lambda : collections.defaultdict(list))
graph = {'relationships': collections.defaultdict(default_list), 'counts': {}, 'exists': {}, 'history': [], 'objects': {}}
n_inputs = template.get('inputs', 1)
text_sample = random.choice(template['text'])
text_sample_index = template['text'].index(text_sample)
tags = re.findall('(<[\\d\\w]*>)', text_sample)
tag_groups = collections.defaultdict(list)
for tag in tags:
group_id = get_tag_group(tag)
tag_groups[group_id].append(tag)
arg_sample = random.choice(filter_objs)
graph_item = arg_sample['graph']
for arg_ind in range(n_inputs):
obj_sample = arg_sample['objects'][arg_ind]
avail_attrs = (obj_sample['optional'] + obj_sample['required'])
for ii in tag_groups[arg_ind][::(- 1)]:
if (mapping(ii) not in avail_attrs):
tag_groups[arg_ind].remove(ii)
text_sample = replace_attribute(text_sample, ii, arg_sample, True)
for attribute in obj_sample['required']:
required_tag = inv_mapping(attribute, arg_ind)
assert (required_tag in tag_groups[arg_ind]), 'A required attribute is missing in template!'
tags_to_keep = [inv_mapping(ii, arg_ind) for ii in obj_sample['required']]
optional_tags = [inv_mapping(ii, arg_ind) for ii in obj_sample['optional']]
optional_tags = [ii for ii in optional_tags if (ii in tag_groups[arg_ind])]
if (len(optional_tags) > 0):
if (len(tags_to_keep) > 0):
n_tags_sample = [0, 1, 2]
else:
n_tags_sample = [1, 2, 3]
n_sample = np.random.choice(n_tags_sample, 1, p=gvars.METAINFO['probabilities'], replace=False)
n_sample = min(n_sample[0], len(optional_tags))
if (n_sample > 0):
tags_to_keep += random.sample(optional_tags, n_sample)
for tag in tag_groups[arg_ind]:
remove = (tag not in tags_to_keep)
text_sample = replace_attribute(text_sample, tag, arg_sample, remove)
if ('objects' in graph_item):
for ii in gvars.METAINFO['attributes']:
if (inv_mapping(ii, arg_ind) not in tags_to_keep):
if (ii in graph_item['objects'][arg_ind]):
del graph_item['objects'][arg_ind][ii]
graph_item['round'] = 0
sample = {}
sample['template_info'] = [copy.deepcopy(template)]
del sample['template_info'][(- 1)]['text']
sample['template_info'][(- 1)]['index'] = text_sample_index
sample['caption'] = text_sample
sample['dialog'] = []
graph['history'].append(graph_item)
sample['graph'] = utils.merge_update_scene_graph(graph, graph_item)
return sample |
def train_model():
(g, train_tensor) = build_model()
with g.as_default():
slim.learning.train(train_tensor, FLAGS.checkpoint_dir, is_chief=(FLAGS.task == 0), master=FLAGS.master, log_every_n_steps=FLAGS.log_every_n_steps, graph=g, number_of_steps=FLAGS.number_of_steps, save_summaries_secs=FLAGS.save_summaries_secs, save_interval_secs=FLAGS.save_interval_secs, init_fn=get_checkpoint_init_fn(), global_step=tf.train.get_global_step()) |
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